SlideShare uma empresa Scribd logo
1 de 54
Baixar para ler offline
Bibliography
[1] F. Yang, H. Lu, M.-H. Yang, Robust superpixel tracking, IEEE Transactions
on Image Processing 23 (4) (2014) 1639–1651.
[2] S. Oron, A. Bar-Hillel, D. Levi, S. Avidan, Locally orderless tracking, Inter-
national Journal of Computer Vision 111 (2) (2015) 213–228.
[3] D. H. Ballard, C. M. Brown, Computer Vision, 1st Edition, Prentice-Hall, New
Jersey, 1982.
[4] A. Blake, A. Zisserman, Visual Reconstruction, 1st Edition, MIT Press, Lon-
don, 1987.
[5] M. M. Trivedi, A. Rosenfeld, On making computers see, IEEE Transactions
on Systems, Man and Cybernetics 19 (6) (1989) 1333–1335.
[6] R. Jain, R. Kasturi, B. G. Schunck, Machine Vision, 1st Edition, McGraw-Hill,
New Delhi, 1995.
[7] G. M. Petersen, Range-finding in the army. How to use range-finders to get
results: The erect and inverted types, Popular Science Monthly 96 (1919)
118–120.
239
[8] R. Sim, J. J. Little, Autonomous vision-based exploration and mapping us-
ing hybrid maps and Rao-Blackwellised particle filters, in: Proceedings of
IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE,
2006, pp. 2082–2089.
[9] A. L. Bovic, Handbook of Image and Video Processing, 1st Edition, Academic
Press, New York, 2000.
[10] X. Li, W. Hu, C. Shen, Z. Zhang, A. Dick, A. V. D. Hengel, A survey of
appearance models in visual object tracking, ACM Transactions on Intelligent
Systems and Technology (TIST) 4 (4) (2013) 1–58.
[11] A. M. Tekalp, Digital Video Processing, 1st Edition, Prentice Hall, New Jersey,
1995.
[12] I. Haritaoglu, D. Harwood, L. S. Davis, W4: Real-time surveillance of peo-
ple and their activities, IEEE Transactions on Pattern Analysis and Machine
Intelligence 22 (8) (2000) 809–830.
[13] R. T. Collins, A. J. Lipton, T. Kanade, H. Fujiyoshi, D. Duggins, Y. Tsin,
D. Tolliver, N. Enomoto, O. Hasegawa, P. Burt, et al., A system for video
surveillance and monitoring, Technical Report CMU-RI-TR-00-12, Carnegie
Mellon University, Pittsburg (2000).
[14] J. A. Quinn, R. Nakibuule, Traffic flow monitoring in crowded cities., in: AAAI
Spring Symposium: Artificial Intelligence for Development, AAAI, 2010, pp.
73–78.
240
[15] S. Kamijo, Y. Matsushita, K. Ikeuchi, M. Sakauchi, Traffic monitoring and
accident detection at intersections, IEEE Transactions on Intelligent Trans-
portation Systems 1 (2) (2000) 108–118.
[16] J.-C. Tai, S.-T. Tseng, C.-P. Lin, K.-T. Song, Real-time image tracking for
automatic traffic monitoring and enforcement applications, Image and Vision
Computing 22 (6) (2004) 485–501.
[17] A. B. Chan, Z.-S. J. Liang, N. Vasconcelos, Privacy preserving crowd monitor-
ing: Counting people without people models or tracking, in: IEEE Conference
on Computer Vision and Pattern Recognition (CVPR), IEEE, 2008, pp. 1–7.
[18] M. Hashemzadeh, G. Pan, M. Yao, Counting moving people in crowds using
motion statistics of feature-points, Multimedia Tools and Applications 72 (1)
(2014) 453–487.
[19] S. Lenser, M. Veloso, Visual sonar: Fast obstacle avoidance using monocular
vision, in: Proceedings of IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS), Vol. 1, IEEE, 2003, pp. 886–891.
[20] C. Song, H. Zhao, W. Jing, Y. Bi, Robust video stabilization based on bounded
path planning, in: IEEE International Conference on Pattern Recognition
(ICPR), IEEE, 2012, pp. 3684–3687.
[21] F.-S. Chen, C.-M. Fu, C.-L. Huang, Hand gesture recognition using a real-time
tracking method and hidden Markov models, Image and Vision Computing
21 (8) (2003) 745–758.
241
[22] N. D. Binh, E. Shuichi, T. Ejima, Real-time hand tracking and gesture recogni-
tion system, in: Proceding of Graphics Vision and Image Processing (GVIP),
Citeseer, 2005, pp. 19–21.
[23] S. S. Ge, Y. Yang, T. H. Lee, Hand gesture recognition and tracking based
on distributed locally linear embedding, Image and Vision Computing 26 (12)
(2008) 1607–1620.
[24] S. Paschalakis, M. Bober, Real-time face detection and tracking for mobile
videoconferencing, Real-Time Imaging 10 (2) (2004) 81–94.
[25] E. Bardinet, L. D. Cohen, N. Ayache, Tracking and motion analysis of the left
ventricle with deformable superquadrics, Medical Image Analysis 1 (2) (1996)
129–149.
[26] E. Kochavi, D. Goldsher, H. Azhari, Method for rapid MRI needle tracking,
Magnetic Resonance in Medicine 51 (5) (2004) 1083–1087.
[27] P. Mountney, G.-Z. Yang, Soft tissue tracking for minimally invasive surgery:
Learning local deformation online, in: Medical Image Computing and
Computer-Assisted Intervention (MICCAI), Springer, 2008, pp. 364–372.
[28] W. Geng, P. Cosman, C. C. Berry, Z. Feng, W. R. Schafer, Automatic tracking,
feature extraction and classification of C. elegans phenotypes, IEEE Transac-
tions on Biomedical Engineering 51 (10) (2004) 1811–1820.
[29] A. Veeraraghavan, R. Chellappa, M. Srinivasan, Shape-and-behavior encoded
tracking of Bee dances, IEEE Transactions on Pattern Analysis and Machine
Intelligence 30 (3) (2008) 463–476.
242
[30] E. Sahouria, A. Zakhor, A trajectory based video indexing system for street
surveillance, in: IEEE International Conference on Image Processing (ICIP),
IEEE, 1999, pp. 24–28.
[31] J. S. Yuk, K.-Y. K. Wong, R. H. Chung, K. Chow, F. Y. Chin, K. S.
Tsang, Object-based surveillance video retrieval system with real-time index-
ing methodology, in: Image Analysis and Recognition, Springer, 2007, pp.
626–637.
[32] D. A. Forsyth, J. Ponce, Computer Vision: A Modern Approach, 1st Edition,
Prentice Hall, New Jersey, 2003.
[33] R. M. Haralick, L. G. Shapiro, Computer and Robot Vision, 1st Edition,
Addison-Wesley Publishing Company, New York, 1992.
[34] B. K. P. Horn, Robot Vision, 1st Edition, MIT Press, Cambridge, USA, 1986.
[35] A. Yilmaz, O. Javed, M. Shah, Object tracking: A survey, ACM Computing
Surveys 38 (4) (2006) 1–45.
[36] E. Maggio, A. Cavallaro, Video Tracking: Theory and Practice, 1st Edition,
John Wiley & Sons, United Kingdom, 2011.
[37] B. D. Lucas, T. Kanade, et al., An iterative image registration technique
with an application to stereo vision., in: Proceedings of International Joint
Conference in Artificial Intelligence (IJCAI), Vol. 81, IEEE, 1981, pp. 674–
679.
[38] J. Shi, C. Tomasi, Good features to track, in: IEEE Conference on Computer
Vision and Pattern Recognition (CVPR), IEEE, 1994, pp. 593–600.
243
[39] C. J. Veenman, M. J. Reinders, E. Backer, Resolving motion correspondence
for densely moving points, IEEE Transactions on Pattern Analysis and Ma-
chine Intelligence 23 (1) (2001) 54–72.
[40] V. Lepetit, P. Lagger, P. Fua, Randomized trees for real-time keypoint recog-
nition, in: IEEE Conference on Computer Vision and Pattern Recognition
(CVPR), Vol. 2, IEEE, 2005, pp. 775–781.
[41] T. Rémi, M. Bernard, Probabilistic matching algorithm for keypoint based
object tracking using a delaunay triangulation, in: International Workshop on
Image Analysis for Multimedia Interactive Services (WIAMIS), IEEE, 2007,
pp. 1–17.
[42] T. T. H. Tran, E. Marchand, Real-time keypoints matching: application to vi-
sual servoing, in: IEEE International Conference on Robotics and Automation
(ICRA), IEEE, 2007, pp. 3787–3792.
[43] G. Nebehay, R. Pflugfelder, TLM: tracking-learning-matching of keypoints,
in: IEEE International Conference on Distributed Smart Cameras (ICDSC),
IEEE, 2013, pp. 1–6.
[44] B. Babenko, M.-H. Yang, S. Belongie, Robust object tracking with online mul-
tiple instance learning, IEEE Transactions on Pattern Analysis and Machine
Intelligence 33 (8) (2011) 1619–1632.
[45] J. Dou, Q. Qin, Z. Tu, Improved weighted multiple instance learning for object
tracking, Optik-International Journal for Light and Electron Optics 126 (24)
(2015) 5287–5293.
244
[46] G. R. Bradski, Computer vision face tracking for use in a perceptual user
interface, Intel Technology Journal Q2 2 (1998) 1–15.
[47] D. Comaniciu, V. Ramesh, P. Meer, Kernel-based object tracking, IEEE Trans-
actions on Pattern Analysis and Machine Intelligence 25 (5) (2003) 564–577.
[48] X. An, J. Kim, Y. Han, Optimal colour-based mean shift algorithm for tracking
objects, IET Computer Vision 8 (3) (2014) 235–244.
[49] L. Vacchetti, V. Lepetit, P. Fua, Stable real-time 3D tracking using online
and offline information, IEEE Transactions on Pattern Analysis and Machine
Intelligence 26 (10) (2004) 1385–1391.
[50] J. Giebel, D. M. Gavrila, C. Schnörr, A Bayesian framework for multi-cue
3D object tracking, in: European Conference on Computer Vision (ECCV),
Springer, 2004, pp. 241–252.
[51] Y. Park, V. Lepetit, W. Woo, Multiple 3D object tracking for augmented
reality, in: Proceedings of IEEE/ACM International Symposium on Mixed
and Augmented Reality, IEEE, 2008, pp. 117–120.
[52] L. Wang, W. Hu, T. Tan, Recent developments in human motion analysis,
Pattern Recognition 36 (3) (2003) 585–601.
[53] A. Sundaresan, R. Chellappa, Multicamera tracking of articulated human mo-
tion using shape and motion cues, IEEE Transactions on Image Processing,
18 (9) (2009) 2114–2126.
[54] L. Mussi, S. Ivekovic, S. Cagnoni, Markerless articulated human body tracking
from multi-view video with GPU-PSO, in: Evolvable Systems: from Biology
to Hardware, Springer, 2010, pp. 97–108.
245
[55] I. Oikonomidis, N. Kyriazis, A. A. Argyros, Tracking the articulated motion
of two strongly interacting hands, in: IEEE Conference on Computer Vision
and Pattern Recognition (CVPR), IEEE, 2012, pp. 1862–1869.
[56] A. Ali, J. Aggarwal, Segmentation and recognition of continuous human ac-
tivity, in: Proceedings of IEEE Workshop on Detection and Recognition of
Events in Video (DREV), IEEE, 2001, pp. 28–35.
[57] A. Kar, Skeletal tracking using Microsoft kinect, Methodology 1 (2010) 1–11.
[58] L. A. Schwarz, A. Mkhitaryan, D. Mateus, N. Navab, Human skeleton tracking
from depth data using geodesic distances and optical flow, Image and Vision
Computing 30 (3) (2012) 217–226.
[59] N. R. Howe, Silhouette lookup for automatic pose tracking, in: IEEE Con-
ference on Computer Vision and Pattern Recognition Workshop (CVPRW),
IEEE, 2004, pp. 15–22.
[60] B. Rosenhahn, U. Kersting, S. Andrew, T. Brox, R. Klette, H.-P. Seidel, A
silhouette based human motion tracking system, Tectnical Report 1530, CITR,
University of Auckland, New Zealand (2005).
[61] A. Yilmaz, X. Li, M. Shah, Object contour tracking using level sets, in: Asian
Conference on Computer Vision (ACCV), Vol. 1, Springer, 2004, pp. 1–7.
[62] M. Yokoyama, T. Poggio, A contour-based moving object detection and track-
ing, in: Joint IEEE International Workshop on Visual Surveillance and Per-
formance Evaluation of Tracking and Surveillance (WVSPETS), IEEE, 2005,
pp. 271–276.
246
[63] T. Brox, A. Bruhn, N. Papenberg, J. Weickert, High accuracy optical flow esti-
mation based on a theory for warping, in: European Conference on Computer
Vision (ECCV), Springer, 2004, pp. 25–36.
[64] P. Sand, S. Teller, Particle video: Long-range motion estimation using point
trajectories, International Journal of Computer Vision 80 (1) (2008) 72–91.
[65] S. Salti, A. Cavallaro, L. D. Stefano, Adaptive appearance modeling for video
tracking: Survey and evaluation, IEEE Transactions on Image Processing
21 (10) (2012) 4334–4348.
[66] G. Silveira, E. Malis, Real-time visual tracking under arbitrary illumination
changes, in: IEEE Conference on Computer Vision and Pattern Recognition
(CVPR), Vol. 1, IEEE, 2007, pp. 1–6.
[67] J. Ho, K.-C. Lee, M.-H. Yang, D. Kriegman, Visual tracking using learned lin-
ear subspaces, in: IEEE Conference on Computer Vision and Pattern Recog-
nition (CVPR), Vol. 1, IEEE, 2004, pp. 782–789.
[68] Y. Li, On incremental and robust subspace learning, Pattern Recognition
37 (7) (2004) 1509–1518.
[69] D. A. Ross, J. Lim, R.-S. Lin, M.-H. Yang, Incremental learning for robust
visual tracking, International Journal of Computer Vision 77 (13) (2008) 125–
141.
[70] S. Baker, I. Matthews, Lucas-Kanade 20 years on: A unifying framework,
International Journal of Computer Vision 56 (3) (2004) 221–255.
247
[71] H. T. Nguyen, A. W. Smeulders, Fast occluded object tracking by a robust
appearance filter, IEEE Transactions on Pattern Analysis and Machine Intel-
ligence 26 (8) (2004) 1099–1104.
[72] X. Li, W. Hu, Z. Zhang, X. Zhang, G. Luo, Robust visual tracking based on
incremental tensor subspace learning, in: IEEE 11th International Conference
on Computer Vision (ICCV), IEEE, 2007, pp. 1–8.
[73] T. Wang, I. Y. Gu, P. Shi, Object tracking using incremental 2D-PCA learning
and ML estimation, in: IEEE International Conference on Acoustics, Speech
and Signal Processing (ICASSP), Vol. 1, IEEE, 2007, pp. 933–936.
[74] X. Li, W. Hu, Z. Zhang, X. Zhang, M. Zhu, J. Cheng, Visual tracking via in-
cremental log-Euclidean Riemannian subspace learning, in: IEEE Conference
on Computer Vision and Pattern Recognition (CVPR), IEEE, 2008, pp. 1–8.
[75] J. Wen, X. Li, X. Gao, D. Tao, Incremental learning of weighted tensor sub-
space for visual tracking, in: IEEE International Conference on Systems, Man
and Cybernetics (SMC), IEEE, 2009, pp. 3688–3693.
[76] W. Hu, X. Li, X. Zhang, X. Shi, S. Maybank, Z. Zhang, Incremental tensor
subspace learning and its applications to foreground segmentation and track-
ing, International Journal of Computer Vision 91 (3) (2011) 303–327.
[77] M. S. Allili, D. Ziou, Object of interest segmentation and tracking by using
feature selection and active contours, in: IEEE Conference on Computer Vision
and Pattern Recognition (CVPR), IEEE, 2007, pp. 1–8.
248
[78] M. Werlberger, W. Trobin, T. Pock, A. Wedel, D. Cremers, H. Bischof,
Anisotropic Huber-L1 optical flow, in: British Machine Vision Conference
(BMVC), Vol. 1, BMVA Press, 2009, pp. 1–11.
[79] Y. Wu, J. Fan, Contextual flow, in: IEEE Conference on Computer Vision
and Pattern Recognition (CVPR), IEEE, 2009, pp. 33–40.
[80] J. Santner, C. Leistner, A. Saffari, T. Pock, H. Bischof, PROST: parallel robust
online simple tracking, in: IEEE Conference on Computer Vision and Pattern
Recognition (CVPR), IEEE, 2010, pp. 723–730.
[81] Q. Zhao, Z. Yang, H. Tao, Differential Earth Mover’s distance with its applica-
tions to visual tracking, IEEE Transactions on Pattern Analysis and Machine
Intelligence 32 (2) (2010) 274–287.
[82] B. Georgescu, P. Meer, Point matching under large image deformations and
illumination changes, IEEE Transactions on Pattern Analysis and Machine
Intelligence 26 (6) (2004) 674–688.
[83] C. Yang, R. Duraiswami, L. Davis, Efficient mean-shift tracking via a new
similarity measure, in: IEEE Conference on Computer Vision and Pattern
Recognition (CVPR), Vol. 1, IEEE, 2005, pp. 176–183.
[84] S. T. Birchfield, S. Rangarajan, Spatiograms versus histograms for region-
based tracking, in: IEEE Conference on Computer Vision and Pattern Recog-
nition (CVPR), Vol. 2, IEEE, 2005, pp. 1158–1163.
[85] S. T. Birchfield, S. Rangarajan, Spatial histograms for region-based tracking,
Electronics and Telecommunications Research Institute (ETRI) Journal 29 (5)
(2007) 697–699.
249
[86] B. R. Venkatesh, A. Makur, Kernel-based spatial-color modeling for fast mov-
ing object tracking, in: IEEE International Conference on Acoustics, Speech
and Signal Processing (ICASSP), Vol. 1, IEEE, 2007, pp. 901–904.
[87] D.-H. Kim, H.-K. Kim, S.-J. Ko, et al., Spatial color histogram based center
voting method for subsequent object tracking and segmentation, Image and
Vision Computing 29 (12) (2011) 850–860.
[88] A. Adam, E. Rivlin, I. Shimshoni, Robust fragments-based tracking using the
integral histogram, in: IEEE Conference on Computer Vision and Pattern
Recognition (CVPR), Vol. 1, IEEE, 2006, pp. 798–805.
[89] S. S. Nejhum, J. Ho, M.-H. Yang, Online visual tracking with histograms and
articulating blocks, Computer Vision and Image Understanding 114 (8) (2010)
901–914.
[90] W. Zhong, H. Lu, M.-H. Yang, Robust object tracking via sparsity-based
collaborative model, in: IEEE Conference on Computer Vision and Pattern
Recognition (CVPR), IEEE, 2012, pp. 1838–1845.
[91] V. Takala, M. Pietikainen, Multi-object tracking using color, texture and
motion, in: IEEE Conference on Computer Vision and Pattern Recognition
(CVPR), IEEE, 2007, pp. 1–7.
[92] J. Ning, L. Zhang, D. Zhang, C. Wu, Robust object tracking using joint color-
texture histogram, International Journal of Pattern Recognition and Artificial
Intelligence 23 (7) (2009) 1245–1263.
250
[93] M. Diwakar, P. K. Patel, K. Gupta, C. Chauhan, Object tracking using joint
enhanced color-texture histogram, in: IEEE 2nd International Conference on
Image Information Processing (ICIIP), IEEE, 2013, pp. 160–165.
[94] S. Birchfield, Elliptical head tracking using intensity gradients and color his-
tograms, in: IEEE Conference on Computer Vision and Pattern Recognition
(CVPR), IEEE, 1998, pp. 232–237.
[95] I. Haritaoglu, M. Flickner, Detection and tracking of shopping groups in stores,
in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR),
Vol. 1, IEEE, 2001, pp. 431–438.
[96] J. Wang, Y. Yagi, Integrating color and shape-texture features for adap-
tive real-time object tracking, IEEE Transactions on Image Processing 17 (2)
(2008) 235–240.
[97] A. Gelzinis, A. Verikas, M. Bacauskiene, Increasing the discrimination power
of the co-occurrence matrix-based features, Pattern Recognition 40 (9) (2007)
2367–2372.
[98] R. M. Haralick, K. Shanmugam, I. H. Dinstein, Textural features for im-
age classification, IEEE Transactions on Systems, Man and Cybernetics 3 (6)
(1973) 610–621.
[99] F. Porikli, O. Tuzel, P. Meer, Covariance tracking using model update based
on Lie Algebra, in: IEEE Conference on Computer Vision and Pattern Recog-
nition (CVPR), Vol. 1, IEEE, 2006, pp. 728–735.
251
[100] O. Tuzel, F. Porikli, P. Meer, Region covariance: A fast descriptor for detection
and classification, in: European Conference on Computer Vision (ECCV),
Springer, 2006, pp. 589–600.
[101] G. Li, D. Liang, Q. Huang, S. Jiang, W. Gao, Object tracking using incre-
mental 2D-LDA learning and Bayes inference, in: IEEE 15th International
Conference on Image Processing (ICIP), IEEE, 2008, pp. 1568–1571.
[102] Y. Wu, J. Cheng, J. Wang, H. Lu, Real-time visual tracking via incremental
covariance tensor learning, in: IEEE 12th International Conference on Com-
puter Vision (CVPR), IEEE, 2009, pp. 1631–1638.
[103] X. Hong, H. Chang, S. Shan, B. Zhong, X. Chen, W. Gao, Sigma set based
implicit online learning for object tracking, IEEE Signal Processing Letters
17 (9) (2010) 807–810.
[104] I. Austvoll, B. Kwolek, Region covariance matrix-based object tracking with
occlusions handling, in: Computer Vision and Graphics, Springer, 2010, pp.
201–208.
[105] W. Hu, X. Li, W. Luo, X. Zhang, S. Maybank, Z. Zhang, Single and multiple
object tracking using log-Euclidean Riemannian subspace and block-division
appearance model, IEEE Transactions on Pattern Analysis and Machine In-
telligence 34 (12) (2012) 2420–2440.
[106] Y. Wu, J. Cheng, J. Wang, H. Lu, J. Wang, H. Ling, E. Blasch, L. Bai,
Real-time probabilistic covariance tracking with efficient model update, IEEE
Transactions on Image Processing 21 (5) (2012) 2824–2837.
252
[107] C. He, Y. F. Zheng, S. C. Ahalt, Object tracking using the Gabor wavelet
transform and the golden section algorithm, IEEE Transactions on Multimedia
4 (4) (2002) 528–538.
[108] A. Mojaev, A. Zell, Image decomposition and tracking with Gabor wavelets,
Machine Intelligence and Robotics Control 1 (1) (2003) 3–9.
[109] A. Khare, U. S. Tiwary, Daubechies complex wavelet transform based moving
object tracking, in: IEEE Symposium on Computational Intelligence in Image
and Signal Processing (CIISP), IEEE, 2007, pp. 36–40.
[110] M. Li, Z. Zhang, K. Huang, T. Tan, Robust visual tracking based on simpli-
fied biologically inspired features, in: IEEE 16th International Conference on
Image Processing (ICIP), IEEE, 2009, pp. 4113–4116.
[111] O. Prakash, A. Khare, Tracking of non-rigid object in complex wavelet domain,
Journal of Signal and Information Processing 2 (2) (2011) 105–111.
[112] X. Li, A. Dick, C. Shen, D. H. A. Van, H. Wang, Incremental learning of 3D-
DCT compact representations for robust visual tracking, IEEE Transactions
on Pattern Analysis and Machine Intelligence 35 (4) (2013) 863–881.
[113] L. Yu, X. Zhang, L. Zheng, A new object tracking algorithm based on the fast
discrete curvelet transform, International Journal of Signal Processing, Image
Processing and Pattern Recognition 7 (1) (2014) 53–64.
[114] N. Paragios, R. Deriche, Geodesic active contours and level sets for the detec-
tion and tracking of moving objects, IEEE Transactions on Pattern Analysis
and Machine Intelligence 22 (3) (2000) 266–280.
253
[115] D. Cremers, Dynamical statistical shape priors for level set-based tracking,
IEEE Transactions on Pattern Analysis and Machine Intelligence 28 (8) (2006)
1262–1273.
[116] M. S. Allili, D. Ziou, Object of interest segmentation and tracking by using
feature selection and active contours, in: IEEE Conference on Computer Vision
and Pattern Recognition (CVPR), IEEE, 2007, pp. 1–8.
[117] N. Vaswani, Y. Rathi, A. Yezzi, A. Tannenbaum, PF-MT with an interpolation
effective basis for tracking local contour deformations, IEEE Transactions on
Image Processing 19 (4) (2008) 841–857.
[118] D.-X. Lai, Y.-H. Chang, Z.-H. Zhong, Active contour tracking of moving ob-
jects using edge flows and Ant colony optimization in video sequences, in:
Advances in Image and Video Technology, Springer, 2009, pp. 1104–1116.
[119] C.-H. Chuang, Y.-L. Chao, Z.-P. Li, Moving object segmentation and tracking
using active contour and color classification models, in: IEEE International
Symposium on Multimedia (ISM), IEEE, 2010, pp. 73–80.
[120] X. Sun, H. Yao, S. Zhang, A novel supervised level set method for non-rigid
object tracking, in: IEEE Conference on Computer Vision and Pattern Recog-
nition (CVPR), IEEE, 2011, pp. 3393–3400.
[121] X. Mei, H. Ling, Robust visual tracking and vehicle classification via sparse
representation, IEEE Transactions on Pattern Analysis and Machine Intelli-
gence 33 (11) (2011) 2259–2272.
254
[122] F. Chen, Q. Wang, S. Wang, W. Zhang, W. Xu, Object tracking via appear-
ance modeling and sparse representation, Image and Vision Computing 29 (11)
(2011) 787–796.
[123] Z. Han, J. Jiao, B. Zhang, Q. Ye, J. Liu, Visual object tracking via sample-
based adaptive sparse representation (AdaSR), Pattern Recognition 44 (9)
(2011) 2170–2183.
[124] T. Bai, Y. F. Li, Robust visual tracking with structured sparse representation
appearance model, Pattern Recognition 45 (6) (2012) 2390–2404.
[125] Q. Wang, F. Chen, W. Xu, M.-H. Yang, Online discriminative object track-
ing with local sparse representation, in: IEEE Workshop on Applications of
Computer Vision (WACV), IEEE, 2012, pp. 425–432.
[126] T. Zhang, B. Ghanem, S. Liu, N. Ahuja, Low-rank sparse learning for ro-
bust visual tracking, in: European Conference on Computer Vision (ECCV),
Springer, 2012, pp. 470–484.
[127] T. Zhang, B. Ghanem, S. Liu, N. Ahuja, Robust visual tracking via structured
multi-task sparse learning, International Journal of Computer Vision 101 (2)
(2013) 367–383.
[128] Y. Bai, M. Tang, Object tracking via robust multitask sparse representation,
IEEE Signal Processing Letters 21 (8) (2014) 909–913.
[129] D. G. Lowe, Distinctive image features from scale-invariant keypoints, Inter-
national Journal of Computer Vision 60 (2) (2004) 91–110.
255
[130] F. Tang, H. Tao, Probabilistic object tracking with dynamic attributed rela-
tional feature graph, IEEE Transactions on Circuits and Systems for Video
Technology 18 (8) (2008) 1064–1074.
[131] H. Zhou, Y. Yuan, C. Shi, Object tracking using SIFT features and mean shift,
Computer Vision and Image Understanding 113 (3) (2009) 345–352.
[132] Y. Yan, J. Wang, C. Li, Z. Wu, Object tracking using SIFT features in a parti-
cle filter, in: IEEE 3rd International Conference on Communication Software
and Networks (ICCSN), IEEE, 2011, pp. 384–388.
[133] S.-W. Ha, Y.-H. Moon, Multiple object tracking using SIFT features and lo-
cation matching, International Journal of Smart Home 5 (4) (2011) 17–26.
[134] H. Bay, T. Tuytelaars, G. L. Van, SURF: Speeded up robust features, in:
European Conference on Computer vision (ECCV), Springer, 2006, pp. 404–
417.
[135] H. Bay, T. Tuytelaars, G. L. Van, Speeded-Up Robust Features (SURF), Com-
puter Vision and Image Understanding 110 (3) (2008) 346–359.
[136] W. He, T. Yamashita, H. Lu, S. Lao, SURF tracking, in: IEEE 12th Interna-
tional Conference on Computer Vision (ICCV), IEEE, 2009, pp. 1586–1592.
[137] D.-N. Ta, W.-C. Chen, N. Gelfand, K. Pulli, SURFTrac: Efficient tracking and
continuous object recognition using local feature descriptors, in: IEEE Con-
ference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2009,
pp. 2937–2944.
256
[138] J. Zhang, J. Fang, J. Lu, Mean-shift algorithm integrating with SURF for
tracking, in: IEEE 7th International Conference on Natural Computation
(ICNC), Vol. 2, IEEE, 2011, pp. 960–963.
[139] H. Shuo, W. Na, S. Huajun, Object tracking method based on SURF, AASRI
Procedia 3 (2012) 351–356.
[140] Z. Zhou, X. Ou, J. Xu, SURF feature detection method used in object track-
ing, in: IEEE International Conference on Machine Learning and Cybernetics
(ICMLC), Vol. 4, IEEE, 2013, pp. 1865–1868.
[141] J. Sivic, F. Schaffalitzky, A. Zisserman, Object level grouping for video shots,
in: European Conference on Computer Vision (ECCV), Springer, 2004, pp.
85–98.
[142] J. Sivic, F. Schaffalitzky, A. Zisserman, Object level grouping for video shots,
International Journal of Computer Vision 67 (2) (2006) 189–210.
[143] M. Donoser, H. Bischof, Efficient Maximally Stable Extremal Region (MSER)
tracking, in: IEEE Conference on Computer Vision and Pattern Recognition
(CVPR), Vol. 1, IEEE, 2006, pp. 553–560.
[144] S. Tran, L. Davis, Robust object trackinng with regional affine invariant fea-
tures, in: IEEE 11th International Conference on Computer Vision (ICCV),
IEEE, 2007, pp. 1–8.
[145] P. Tissainayagam, D. Suter, Object tracking in image sequences using point
features, Pattern Recognition 38 (1) (2005) 105–113.
257
[146] M. Grabner, H. Grabner, H. Bischof, Learning features for tracking, in: IEEE
Conference on Computer Vision and Pattern Recognition (CVPR), IEEE,
2007, pp. 1–8.
[147] N. Li, L. Liu, D. Xu, Corner feature based object tracking using adaptive
Kalman filter, in: IEEE 9th International Conference on Signal Processing
(ICSP), IEEE, 2008, pp. 1432–1435.
[148] Z. Kim, Real time object tracking based on dynamic feature grouping with
background subtraction, in: IEEE Conference on Computer Vision and Pat-
tern Recognition (CVPR), IEEE, 2008, pp. 1–8.
[149] S. E. Palmer, Vision Science: Photons to Phenomenology, 1st Edition, MIT
Press, London, 1999.
[150] J. M. Wolfe, Guided search 2.0 A revised model of visual search, Psychonomic
Bulletin & Review 1 (2) (1994) 202–238.
[151] S. Li, M. C. Lee, Fast visual tracking using motion saliency in video, in:
IEEE International Conference on Acoustics, Speech and Signal Processing
(ICASSP), Vol. 1, IEEE, 2007, pp. 1073–1076.
[152] S. Zhang, F. Stentiford, A saliency based object tracking method, in: IEEE In-
ternational Workshop on Content-Based Multimedia Indexing (CBMI), IEEE,
2008, pp. 512–517.
[153] G. Zhang, Z. Yuan, N. Zheng, X. Sheng, T. Liu, Visual saliency based object
tracking, in: Asian Conference on Computer Vision (ACCV), Springer, 2009,
pp. 193–203.
258
[154] D. Sidibé, D. Fofi, F. Mériaudeau, Using visual saliency for object tracking
with particle filters, in: 18th IEEE European Conference on Signal Processing
(ECSP), IEEE, 2010, pp. 1776–1780.
[155] V. Mahadevan, N. Vasconcelos, Biologically inspired object tracking using
center-surround saliency mechanisms, IEEE Transactions on Pattern Analysis
and Machine Intelligence 35 (3) (2013) 541–554.
[156] D. Zhang, W. Li, M. Sun, H. Yu, Saliency map for object tracking, Interna-
tional Journal of Signal Processing, Image Processing and Pattern Recognition
8 (10) (2015) 233–240.
[157] S. Hong, T. You, S. Kwak, B. Han, Online tracking by learning dis-
criminative saliency map with convolutional neural network, arXiv preprint
arXiv:1502.06796 (2015) 1–10.
[158] X. Ren, J. Malik, Tracking as repeated figure/ground segmentation, in: IEEE
Conference on Computer Vision and Pattern Recognition (CVPR), IEEE,
2007, pp. 1–8.
[159] Z. Yin, R. T. Collins, Shape constrained figure-ground segmentation and
tracking, in: IEEE Conference on Computer Vision and Pattern Recognition
(CVPR), IEEE, 2009, pp. 731–738.
[160] F. Li, T. Kim, A. Humayun, D. Tsai, J. Rehg, Video segmentation by tracking
many figure-ground segments, in: IEEE International Conference on Computer
Vision (ICCV), IEEE, 2013, pp. 2192–2199.
259
[161] R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, S. Susstrunk, SLIC super-
pixels compared to state-of-the-art superpixel methods, IEEE Transactions on
Pattern Analysis and Machine Intelligence 34 (11) (2012) 2274–2282.
[162] S. Wang, H. Lu, F. Yang, M.-H. Yang, Superpixel tracking, in: IEEE Interna-
tional Conference on Computer Vision (ICCV), IEEE, 2011, pp. 1323–1330.
[163] W. Wang, R. Nevatia, Robust object tracking using constellation model with
superpixel, in: Asian Conference on Computer Vision (ACCV), Springer, 2012,
pp. 191–204.
[164] Z. Cai, L. Wen, Z. Lei, N. Vasconcelos, S. Z. Li, Robust deformable and
occluded object tracking with dynamic graph, IEEE Transactions on Image
Processing 23 (12) (2014) 5497–5509.
[165] Z. Lin, L. S. Davis, D. Doermann, D. DeMenthon, Hierarchical part-template
matching for human detection and segmentation, in: IEEE 11th International
Conference on Computer Vision (ICCV), IEEE, 2007, pp. 1–8.
[166] F. Pernici, B. A. Del, Object tracking by oversampling local features, IEEE
Transactions on Pattern Analysis and Machine Intelligence 36 (12) (2014)
2538–2551.
[167] T. Ojala, M. Pietikäinen, T. Mäenpää, Multiresolution gray-scale and rotation
invariant texture classification with local binary patterns, IEEE Transactions
on Pattern Analysis and Machine Intelligence 24 (7) (2002) 971–987.
[168] X. Tan, B. Triggs, Enhanced local texture feature sets for face recognition
under difficult lighting conditions, IEEE Transactions on Image Processing
19 (6) (2010) 1635–1650.
260
[169] N. Dalal, B. Triggs, Histograms of oriented gradients for human detection,
in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR),
Vol. 1, IEEE, 2005, pp. 886–893.
[170] H. T. Nguyen, A. Smeulders, Tracking aspects of the foreground against the
background, in: European Conference on Computer Vision (ECCV), Springer,
2004, pp. 446–456.
[171] S. Avidan, Support vector tracking, IEEE Transactions on Pattern Analysis
and Machine Intelligence 26 (8) (2004) 1064–1072.
[172] H. Grabner, H. Bischof, On-line boosting and vision, in: IEEE Conference on
Computer Vision and Pattern Recognition (CVPR), Vol. 1, IEEE, 2006, pp.
260–267.
[173] H. Grabner, M. Grabner, H. Bischof, Real-time tracking via on-line boosting,
in: Proceedings of British Machine Vision Conference (BMVC), Vol. 1, BMVA
Press, 2006, pp. 1–10.
[174] Y. Freund, R. Schapire, N. Abe, A short introduction to boosting, Journal-
Japanese Society for Artificial Intelligence 14 (5) (1999) 771–780.
[175] X. Liu, T. Yu, Gradient feature selection for online boosting, in: IEEE 11th
International Conference on Computer Vision (ICCV), IEEE, 2007, pp. 1–8.
[176] S. Avidan, Ensemble tracking, IEEE Transactions on Pattern Analysis and
Machine Intelligence 29 (2) (2007) 261–271.
[177] T. Parag, F. Porikli, A. Elgammal, Boosting adaptive linear weak classifiers
for online learning and tracking, in: IEEE Conference on Computer Vision
and Pattern Recognition (CVPR), IEEE, 2008, pp. 1–8.
261
[178] I. Visentini, L. Snidaro, G. L. Foresti, Dynamic ensemble for target tracking,
in: 8th International Workshop on Visual Surveillance (VS), 2008, pp. 1–8.
[179] C. Leistner, A. Saffari, P. M. Roth, H. Bischof, On robustness of on-line
boosting-A competitive study, in: IEEE 12th International Conference on
Computer Vision Workshops (ICCVW), IEEE, 2009, pp. 1362–1369.
[180] A. Blum, T. Mitchell, Combining labeled and unlabeled data with co-training,
in: Proceedings of 11th Annual Conference on Computational Learning The-
ory, ACM, 1998, pp. 92–100.
[181] X. Zhu, Semi-supervised learning literature survey, Tectnical Report 1530,
University of Wisconsin, Madison (2007).
[182] O. Chapelle, B. Scholkopf, A. Zien, Semi-Supervised Learning, 1st Edition,
MIT Press, Cambridge, 2006.
[183] H. Grabner, C. Leistner, H. Bischof, Semi-supervised on-line boosting for
robust tracking, in: European Conference on Computer Vision (ECCV),
Springer, 2008, pp. 234–247.
[184] R. Liu, J. Cheng, H. Lu, A robust boosting tracker with minimum error bound
in a co-training framework., in: IEEE 12th International Conference on Com-
puter Vision (ICCV), IEEE, 2009, pp. 1459–1466.
[185] K. Zhang, H. Song, Real-time visual tracking via online weighted multiple
instance learning, Pattern Recognition 46 (1) (2013) 397–411.
[186] M. Li, J. T. Kwok, B.-L. Lu, Online multiple instance learning with no regret,
in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR),
IEEE, 2010, pp. 1395–1401.
262
[187] L. J. Wang, H. Zhang, Visual tracking based on an improved online multi-
ple instance learning algorithm, Computational Intelligence and Neuroscience
2016 (2015) 1–9.
[188] C. Xu, W. Tao, Z. Meng, Z. Feng, Robust visual tracking via online multiple
instance learning with Fisher information, Pattern Recognition 48 (12) (2015)
3917–3926.
[189] B. Zeisl, C. Leistner, A. Saffari, H. Bischof, On-line semi-supervised multiple-
instance boosting, in: IEEE Conference on Computer Vision and Pattern
Recognition (CVPR), IEEE, 2010, pp. 1879–1879.
[190] G. Li, Q. Huang, L. Qin, S. Jiang, SSOCBT: A robust semi-supervised online
CovBoost tracker that uses samples differently, IEEE Transactions on Circuits
and Systems for Video Technology 23 (4) (2013) 695–709.
[191] S. J. Pan, Q. Yang, A survey on transfer learning, IEEE Transactions on
Knowledge and Data Engineering 22 (10) (2010) 1345–1359.
[192] W. Luo, X. Li, W. Li, W. Hu, Robust visual tracking via transfer learning, in:
IEEE 18th International Conference on Image Processing (ICIP), IEEE, 2011,
pp. 485–488.
[193] C. Gao, N. Sang, R. Huang, Online transfer boosting for object tracking, in:
IEEE 21st International Conference on Pattern Recognition (ICPR), IEEE,
2012, pp. 906–909.
[194] Q. Wang, F. Chen, J. Yang, W. Xu, M.-H. Yang, Transferring visual prior for
online object tracking, IEEE Transactions on Image Processing 21 (7) (2012)
3296–3305.
263
[195] Z. Dan, N. Sang, R. Huang, S. Sun, Instance transfer boosting for object
tracking, Optik-International Journal for Light and Electron Optics 124 (18)
(2013) 3446–3450.
[196] J. Gao, H. Ling, W. Hu, J. Xing, Transfer learning based visual tracking with
Gaussian processes regression, in: European Conference on Computer Vision
(ECCV), Springer, 2014, pp. 188–203.
[197] N. Wang, S. Li, A. Gupta, D.-Y. Yeung, Transferring rich feature hierarchies
for robust visual tracking, arXiv preprint arXiv:1501.04587 (2015) 1–9.
[198] L. Breiman, Random forests, Machine Learning 45 (1) (2001) 5–32.
[199] V. Lepetit, P. Fua, Keypoint recognition using randomized trees, IEEE Trans-
actions on Pattern Analysis and Machine Intelligence 28 (9) (2006) 1465–1479.
[200] J. Shotton, M. Johnson, R. Cipolla, Semantic texton forests for image cate-
gorization and segmentation, in: IEEE Conference on Computer Vision and
Pattern Recognition (CVPR), IEEE, 2008, pp. 1–8.
[201] M. Özuysal, M. Calonder, V. Lepetit, P. Fua, Fast keypoint recognition using
random ferns, IEEE Transactions on Pattern Analysis and Machine Intelli-
gence 32 (3) (2010) 448–461.
[202] A. Saffari, C. Leistner, J. Santner, M. Godec, H. Bischof, On-line random
forests, in: IEEE 12th International Conference on Computer Vision Work-
shops (ICCVW), IEEE, 2009, pp. 1393–1400.
[203] M. Godec, C. Leistner, A. Saffari, H. Bischof, On-line random Naive Bayes
for tracking, in: IEEE 20th International Conference on Pattern Recognition
(ICPR), IEEE, 2010, pp. 3545–3548.
264
[204] C. Leistner, A. Saffari, H. Bischof, MIForests: Multiple-instance learning with
randomized trees, in: European Conference on Computer Vision (ECCV),
Springer, 2010, pp. 29–42.
[205] X. Shi, X. Zhang, Y. Liu, W. Hu, H. Ling, Multi-cue based multi-target track-
ing using online random forests, in: IEEE International Conference on Acous-
tics, Speech and Signal Processing (ICASSP), IEEE, 2011, pp. 1185–1188.
[206] J. Gall, A. Yao, N. Razavi, G. L. Van, V. Lempitsky, Hough forests for object
detection, tracking, and action recognition, IEEE Transactions on Pattern
Analysis and Machine Intelligence 33 (11) (2011) 2188–2202.
[207] C. Rao, C. Yao, X. Bai, W. Qiu, W. Liu, Online random ferns for robust vi-
sual tracking, in: IEEE 21st International Conference on Pattern Recognition
(ICPR), IEEE, 2012, pp. 1447–1450.
[208] P. Deng, L. Zhou, B. Wang, Visual tracking based on local patches and ferns
forest, in: IEEE 12th International Conference on Signal Processing (ICSP),
IEEE, 2014, pp. 760–763.
[209] R.-S. Lin, M.-H. Yang, S. E. Levinson, Object tracking using incremental
Fisher discriminant analysis, in: Proceedings of the IEEE 17th International
Conference on Pattern Recognition (ICPR), Vol. 2, IEEE, 2004, pp. 757–760.
[210] Z. Xu, P. Shi, X. Xu, Adaptive subclass discriminant analysis color space learn-
ing for visual tracking, in: Pacific-Rim Conference on Multimedia, Springer,
2008, pp. 902–905.
265
[211] J. Wen, X. Gao, Y. Yuan, D. Tao, J. Li, Incremental tensor biased discriminant
analysis: A new color-based visual tracking method, Neurocomputing 73 (4)
(2010) 827–839.
[212] X. Wang, G. Hua, T. X. Han, Discriminative tracking by metric learning,
in: European Conference on Computer Vision (ECCV), Springer, 2010, pp.
200–214.
[213] N. Jiang, W. Liu, H. Su, Y. Wu, Tracking low resolution objects by metric
preservation, in: IEEE Conference on Computer Vision and Pattern Recogni-
tion (CVPR), IEEE, 2011, pp. 1329–1336.
[214] Y. Cong, J. Yuan, Y. Tang, Object tracking via online metric learning, in:
IEEE 19th International Conference on Image Processing (ICIP), IEEE, 2012,
pp. 417–420.
[215] N. Jiang, W. Liu, Y. Wu, Order determination and sparsity-regularized metric
learning adaptive visual tracking, in: IEEE Conference on Computer Vision
and Pattern Recognition (CVPR), IEEE, 2012, pp. 1956–1963.
[216] C. Gao, F. Chen, J.-G. Yu, R. Huang, N. Sang, Exemplar-based linear discrim-
inant analysis for robust object tracking, in: IEEE International Conference
on Image Processing (ICIP), IEEE, 2014, pp. 388–392.
[217] A. Krizhevsky, I. Sutskever, G. E. Hinton, Imagenet classification with deep
convolutional neural networks, in: Advances in Neural Information Processing
Systems (NIPS), NIPS, 2012, pp. 1097–1105.
[218] G. Hinton, L. Deng, D. Yu, G. E. Dahl, A.-R. Mohamed, N. Jaitly, A. Senior,
V. Vanhoucke, P. Nguyen, T. N. Sainath, et al., Deep neural networks for
266
acoustic modeling in speech recognition: The shared views of four research
groups, IEEE Signal Processing Magazine 29 (6) (2012) 82–97.
[219] N. Wang, D.-Y. Yeung, Learning a deep compact image representation for vi-
sual tracking, in: Advances in Neural Information Processing Systems (NIPS),
NIPS, 2013, pp. 809–817.
[220] J. Jin, A. Dundar, J. Bates, C. Farabet, E. Culurciello, Tracking with deep
neural networks, in: IEEE 47th Annual Conference on Information Sciences
and Systems (CISS), IEEE, 2013, pp. 1–5.
[221] H. Li, Y. Li, F. Porikli, et al., Deeptrack: Learning discriminative feature rep-
resentations by convolutional neural networks for visual tracking., in: British
Machine Vision Conference (BMVC), Vol. 1, BMVA Press, 2014, pp. 1–11.
[222] C. Ma, J.-B. Huang, X. Yang, M.-H. Yang, Hierarchical convolutional features
for visual tracking, in: Proceedings of the IEEE International Conference on
Computer Vision (ICCV), IEEE, 2015, pp. 3074–3082.
[223] D. Hu, X. Zhou, J. Wu, Visual tracking based on convolutional deep belief
network, in: Advanced Parallel Processing Technologies, Springer, 2015, pp.
103–115.
[224] P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, P.-A. Manzagol, Stacked de-
noising autoencoders: Learning useful representations in a deep network with a
local denoising criterion, The Journal of Machine Learning Research 11 (2010)
3371–3408.
[225] F. Yang, H. Lu, Y.-W. Chen, Bag of features tracking, in: IEEE 20th Interna-
tional Conference on Pattern Recognition (ICPR), IEEE, 2010, pp. 153–156.
267
[226] J. Gall, N. Razavi, L. Van Gool, On-line adaption of class-specific codebooks
for instance tracking, in: Proceedings of the British Machine Vision Conference
(BMVC), BMVA Press, 2010, pp. 1–12.
[227] Q. Zhong, Z. Qingqing, G. Tengfei, Moving object tracking based on codebook
and particle filter, Procedia Engineering 29 (2012) 174–178.
[228] F. Yang, H.-H. Lu, W. Zhang, G.-M. Yang, Visual tracking via bag of features,
IET Image Processing 6 (2) (2012) 115–128.
[229] F. Yang, H. Lu, M.-H. Yang, Learning structured visual dictionary for object
tracking, Image and Vision Computing 31 (12) (2013) 992–999.
[230] T. Ren, Z. Qiu, Y. Liu, T. Yu, J. Bei, Soft-assigned bag of features for object
tracking, Multimedia Systems 21 (2) (2015) 189–205.
[231] S. Hare, A. Saffari, P. H. Torr, Struck: Structured output tracking with kernels,
in: IEEE International Conference on Computer Vision (ICCV), IEEE, 2011,
pp. 263–270.
[232] R. Yao, Q. Shi, C. Shen, Y. Zhang, A. van den Hengel, Robust tracking with
weighted online structured learning, in: European Conference on Computer
Vision (ECCV), Springer, 2012, pp. 158–172.
[233] R. Yao, Q. Shi, C. Shen, Y. Zhang, A. Hengel, Part-based visual tracking
with online latent structural learning, in: Proceedings of IEEE Conference on
Computer Vision and Pattern Recognition (CVPR), IEEE, 2013, pp. 2363–
2370.
268
[234] Y. Bai, M. Tang, Robust visual tracking via ranking SVM, in: IEEE 18th
International Conference on Image Processing (ICIP), IEEE, 2011, pp. 517–
520.
[235] Y. Bai, M. Tang, Robust tracking via weakly supervised ranking SVM, in:
IEEE Conference on Computer Vision and Pattern Recognition (CVPR),
IEEE, 2012, pp. 1854–1861.
[236] S. J. McKenna, Y. Raja, S. Gong, Tracking colour objects using adaptive
mixture models, Image and Vision Computing 17 (3) (1999) 225–231.
[237] C. Stauffer, W. E. L. Grimson, Adaptive background mixture models for real-
time tracking, in: IEEE Conference on Computer Vision and Pattern Recog-
nition (CVPR), Vol. 2, IEEE, 1999, pp. 246–252.
[238] C. Stauffer, W. E. L. Grimson, Learning patterns of activity using real-time
tracking, IEEE Transactions on Pattern Analysis and Machine Intelligence
22 (8) (2000) 747–757.
[239] P. KaewTraKulPong, R. Bowden, An improved adaptive background mixture
model for real-time tracking with shadow detection, in: Video-based Surveil-
lance Systems, Springer, 2002, pp. 135–144.
[240] B. Han, L. Davis, On-line density-based appearance modeling for object track-
ing, in: IEEE 10th International Conference on Computer Vision (ICCV),
Vol. 2, IEEE, 2005, pp. 1492–1499.
[241] T. Yu, Y. Wu, Differential tracking based on spatial-appearance model (SAM),
in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR),
Vol. 1, IEEE, 2006, pp. 720–727.
269
[242] R. Sicre, H. Nicolas, Improved Gaussian mixture model for the task of object
tracking, in: Computer Analysis of Images and Patterns, Springer, 2011, pp.
389–396.
[243] V. Karavasilis, C. Nikou, A. Likas, Visual tracking using the Earth Mover’s
distance between Gaussian mixtures and Kalman filtering, Image and Vision
Computing 29 (5) (2011) 295–305.
[244] H. Wang, D. Suter, K. Schindler, C. Shen, Adaptive object tracking based
on an effective appearance filter, IEEE Transactions on Pattern Analysis and
Machine Intelligence 29 (9) (2007) 1661–1667.
[245] A. Bhattacharyya, On a measure of divergence between two statistical pop-
ulations defined by their probability distribution, Bulletin of the Calcutta
Mathematical Society 35 (1) (1943) 99–109.
[246] I. Leichter, M. Lindenbaum, E. Rivlin, Mean shift tracking with multiple ref-
erence color histograms, Computer Vision and Image Understanding 114 (3)
(2010) 400–408.
[247] I. Leichter, M. Lindenbaum, E. Rivlin, Tracking by affine kernel transforma-
tions using color and boundary cues, IEEE Transactions on Pattern Analysis
and Machine Intelligence 31 (1) (2009) 164–171.
[248] A. Babaeian, S. Rastegar, M. Bandarabadi, M. Rezaei, Mean shift-based object
tracking with multiple features, in: IEEE 41st Southeastern Symposium on
System Theory (SSST), IEEE, 2009, pp. 68–72.
[249] H. Zhou, Y. Yuan, C. Shi, Object tracking using SIFT features and mean shift,
Computer Vision and Image Understanding 113 (3) (2009) 345–352.
270
[250] J. G. Allen, R. Y. Xu, J. S. Jin, Object tracking using Camshift algorithm and
multiple quantized feature spaces, in: Proceedings of the Pan-Sydney Area
Workshop on Visual Information Processing, Australian Computer Society,
Inc., 2004, pp. 3–7.
[251] J. Wang, Y. Yagi, Integrating color and shape-texture features for adap-
tive real-time object tracking, IEEE Transactions on Image Processing 17 (2)
(2008) 235–240.
[252] H. Yin, Y. Chai, S. X. Yang, D. K. Chiu, An improved mean-shift tracking
algorithm based on adaptive multiple feature fusion, in: Informatics in Control
Automation and Robotics, Springer, 2011, pp. 49–62.
[253] X. Zhang, Y. Yue, C. Sha, Object tracking approach based on mean shift
algorithm, Journal of Multimedia 8 (3) (2013) 220–225.
[254] R. T. Collins, Mean-shift blob tracking through scale space, in: IEEE Con-
ference on Computer Vision and Pattern Recognition (CVPR), Vol. 2, IEEE,
2003, pp. 228–234.
[255] C.-W. Juan, J.-S. Hu, A new spatial-color mean-shift object tracking algorithm
with scale and orientation estimation, in: IEEE International Conference on
Robotics and Automation (ICRA), IEEE, 2008, pp. 2265–2270.
[256] J.-S. Hu, C.-W. Juan, J.-J. Wang, A spatial-color mean-shift object tracking
algorithm with scale and orientation estimation, Pattern Recognition Letters
29 (16) (2008) 2165–2173.
271
[257] X. Chen, Y. Zhou, X. Huang, C. Li, Adaptive bandwidth mean shift object
tracking, in: IEEE Conference on Robotics, Automation and Mechatronics,
IEEE, 2008, pp. 1011–1017.
[258] J. Ning, L. Zhang, D. Zhang, C. Wu, Scale and orientation adaptive mean
shift tracking, IET Computer Vision 6 (1) (2012) 52–61.
[259] K. Quast, A. Kaup, Shape adaptive mean shift object tracking using Gaussian
mixture models, in: Analysis, Retrieval and Delivery of Multimedia Content,
Springer, 2013, pp. 107–122.
[260] T. Vojir, J. Noskova, J. Matas, Robust scale-adaptive mean-shift for tracking,
Pattern Recognition Letters 49 (2014) 250–258.
[261] A. Yilmaz, Object tracking by asymmetric kernel mean shift with automatic
scale and orientation selection, in: IEEE Conference on Computer Vision and
Pattern Recognition (CVPR), IEEE, 2007, pp. 1–6.
[262] K. Quast, A. Kaup, Scale and shape adaptive mean shift object tracking in
video sequences, in: 17th European Conference on Signal Processing (ECSP),
IEEE, 2009, pp. 1513–1517.
[263] A. Yilmaz, Kernel-based object tracking using asymmetric kernels with adap-
tive scale and orientation selection, Machine Vision and Applications 22 (2)
(2011) 255–268.
[264] C. Shen, M. J. Brooks, D. H. A. Van, Fast global kernel density mode seek-
ing: Applications to localization and tracking, IEEE Transactions on Image
Processing 16 (5) (2007) 1457–1469.
272
[265] G. Strang, Introduction to Linear Algebra, 4th Edition, Wellesley-Cambridge
Press, Wellesley, MA, 2009.
[266] C. M. Bishop, Pattern Recognition and Machine Learning, 1st Edition,
Springer, Verlag, New York, 2006.
[267] H. Lu, K. N. Plataniotis, A. N. Venetsanopoulos, A survey of multilinear
subspace learning for tensor data, Pattern Recognition 44 (7) (2011) 1540–
1551.
[268] M. J. Black, A. D. Jepson, Eigentracking: Robust matching and tracking of
articulated objects using a view-based representation, International Journal of
Computer Vision 26 (1) (1998) 63–84.
[269] D. Skocaj, A. Leonardis, Weighted and robust incremental method for sub-
space learning, in: IEEE International Conference on Computer Vision
(ICCV), IEEE, 2003, pp. 1494–1501.
[270] D. Wang, H. Lu, Y.-W. Chen, Incremental MPCA for color object tracking, in:
IEEE International Conference on Pattern Recognition (ICPR), IEEE, 2010,
pp. 1751–1754.
[271] L. Wen, Z. Cai, Z. Lei, D. Yi, S. Z. Li, Online spatio-temporal structural
context learning for visual tracking, in: European Conference on Computer
Vision (ECCV), Springer, 2012, pp. 716–729.
[272] T. Wang, I. Y. Gu, P. Shi, Object tracking using incremental 2D-PCA learning
and ML estimation, in: IEEE International Conference on Acoustics, Speech
and Signal Processing (ICASSP), Vol. 1, IEEE, 2007, pp. 933–936.
273
[273] D. Wang, H. Lu, Object tracking via 2D-PCA and regularization, IEEE Signal
Processing Letters 19 (11) (2012) 711–714.
[274] H. Lim, O. I. Camps, M. Sznaier, V. I. Morariu, Dynamic appearance modeling
for human tracking, in: IEEE Conference on Computer Vision and Pattern
Recognition (CVPR), Vol. 1, IEEE, 2006, pp. 751–757.
[275] T.-J. Chin, D. Suter, Incremental kernel principal component analysis, IEEE
Transactions on Image Processing 16 (6) (2007) 1662–1674.
[276] D. P. Huttenlocher, G. A. Klanderman, W. J. Rucklidge, Comparing images
using the Hausdorff distance, IEEE Transactions on Pattern Analysis and
Machine Intelligence 15 (9) (1993) 850–863.
[277] R. T. Rockafellar, R. J.-B. Wets, Variational Analysis, 1st Edition, Springer
Science & Business Media, Verlag, Berlin, Heidelberg, 2009.
[278] G. Mori, J. Malik, Estimating human body configurations using shape context
matching, in: European Conference on Computer Vision (ECCV), Springer,
2002, pp. 666–680.
[279] J. Kang, I. Cohen, G. Medioni, Object reacquisition using invariant appearance
model, in: IEEE International Conference on Pattern Recognition (ICPR),
Vol. 4, IEEE, 2004, pp. 759–762.
[280] Q. Xiaoping, Z. Qiheng, O. Yimin, M. Jiaguang, A method for object track-
ing using shape matching, in: IEEE Workshop on Signal Processing Systems
Design and Implementation, IEEE, 2006, pp. 372–376.
[281] V. Ferrari, F. Jurie, C. Schmid, From images to shape models for object de-
tection, International Journal of Computer Vision 87 (3) (2010) 284–303.
274
[282] Z. Liu, H. Shen, G. Feng, D. Hu, Tracking objects using shape context match-
ing, Neurocomputing 83 (2012) 47–55.
[283] C. G. Zhao, T. G. Zhuang, A hybrid boundary detection algorithm based on
Watershed and Snake, Pattern Recognition Letters 26 (9) (2005) 1256–1265.
[284] Y. Xiang, A. C. Chung, J. Ye, An active contour model for image segmentation
based on elastic interaction, Journal of Computational Physics 219 (1) (2006)
455–476.
[285] Y. Rathi, N. Vaswani, A. Tannenbaum, A. Yezzi, Tracking deforming objects
using particle filtering for geometric active contours, IEEE Transactions on
Pattern Analysis and Machine Intelligence 29 (8) (2007) 1470–1475.
[286] M. Kass, A. Witkin, D. Terzopoulos, Snakes: Active contour models, Interna-
tional Journal of Computer Vision 1 (4) (1988) 321–331.
[287] A. Yilmaz, X. Li, M. Shah, Contour-based object tracking with occlusion han-
dling in video acquired using mobile cameras, IEEE Transactions on Pattern
Analysis and Machine Intelligence 26 (11) (2004) 1531–1536.
[288] M. S. Allili, D. Ziou, Object tracking in videos using adaptive mixture models
and active contours, Neurocomputing 71 (10) (2008) 2001–2011.
[289] L. D. Cohen, On active contour models and balloons, CVGIP: Image Under-
standing 53 (2) (1991) 211–218.
[290] C. Xu, J. L. Prince, Snakes, shapes, and gradient vector flow, IEEE Transac-
tions on Image Processing 7 (3) (1998) 359–369.
275
[291] S. Lefèvre, J.-P. Gérard, A. Piron, N. Vincent, An extended snake model
for real-time multiple object tracking, in: RFAI: International Workshop on
Advanced Concepts for Intelligent Vision Systems, Citeseer, 2002, pp. 268–275.
[292] N. Ray, S. T. Acton, Motion gradient vector flow: An external force for track-
ing rolling Leukocytes with shape and size constrained active contours, IEEE
Transactions on Medical Imaging 23 (12) (2004) 1466–1478.
[293] J.-H. Lee, F. Hua, J. W. Jang, An improved object detection and contour
tracking algorithm based on local curvature, in: Signal Processing, Image
Processing and Pattern Recognition, Springer, 2009, pp. 25–32.
[294] J. Chiverton, X. Xie, M. Mirmehdi, Automatic bootstrapping and tracking of
object contours, IEEE Transactions on Image Processing 21 (3) (2012) 1231–
1245.
[295] J. Ning, L. Zhang, D. Zhang, W. Yu, Joint registration and active contour
segmentation for object tracking, IEEE Transactions on Circuits and Systems
for Video Technology 23 (9) (2013) 1589–1597.
[296] V. Caselles, R. Kimmel, G. Sapiro, Geodesic active contours, International
Journal of Computer Vision 22 (1) (1997) 61–79.
[297] S. Osher, J. A. Sethian, Fronts propagating with curvature-dependent speed:
Algorithms based on Hamilton-Jacobi formulations, Journal of Computational
Physics 79 (1) (1988) 12–49.
[298] N. Paragios, R. Deriche, Geodesic active contours and level sets for the detec-
tion and tracking of moving objects, IEEE Transactions on Pattern Analysis
and Machine Intelligence 22 (3) (2000) 266–280.
276
[299] N. Paragios, O. Mellina-Gottardo, V. Ramesh, Gradient vector flow fast
geodesic active contours, in: IEEE International Conference on Computer
Vision (ICCV), Vol. 1, IEEE, 2001, pp. 67–73.
[300] Y. Shi, W. C. Karl, Real-time tracking using level sets, in: IEEE Conference
on Computer Vision and Pattern Recognition (CVPR), Vol. 2, IEEE, 2005,
pp. 34–41.
[301] T. Brox, M. Rousson, R. Deriche, J. Weickert, Colour, texture, and motion
in level set based segmentation and tracking, Image and Vision Computing
28 (3) (2010) 376–390.
[302] R. F. Gonzalez, R. E. Woods, Digital Image Processing, 3rd Edition, Pearson
Education, Singapore, 2008.
[303] Y. Huang, Y. Huang, H. Niemann, Segmentation-based object tracking using
image warping and Kalman filtering, in: IEEE International Conference on
Image Processing (ICIP), Vol. 3, IEEE, 2002, pp. 601–604.
[304] C. Kim, J.-N. Hwang, Fast and automatic video object segmentation and
tracking for content-based applications, IEEE Transactions on Circuits and
Systems for Video Technology 12 (2) (2002) 122–129.
[305] A. Mittal, L. S. Davis, M2tracker: A multi-view approach to segmenting and
tracking people in a cluttered scene, International Journal of Computer Vision
51 (3) (2003) 189–203.
[306] T. Morimoto, O. Kiriyama, Y. Harada, H. Adachi, T. Koide, H. J. Mattausch,
Object tracking in video pictures based on image segmentation and pattern
277
matching, in: IEEE International Symposium on Circuits and Systems (IS-
CAS), IEEE, 2005, pp. 3215–3218.
[307] C. Wang, L. G. M. De, N. Paragios, Segmentation, ordering and multi-object
tracking using graphical models., in: IEEE International Conference on Com-
puter Vision (ICCV), IEEE, 2009, pp. 747–754.
[308] V. Belagiannis, F. Schubert, N. Navab, S. Ilic, Segmentation based particle fil-
tering for real-time 2D object tracking, in: European Conference on Computer
Vision (ECCV), Springer, 2012, pp. 842–855.
[309] X. Ren, J. Malik, Learning a classification model for segmentation, in: IEEE
International Conference on Computer Vision (ICCV), IEEE, 2003, pp. 10–17.
[310] A. Levinshtein, A. Stere, K. N. Kutulakos, D. J. Fleet, S. J. Dickinson, K. Sid-
diqi, Turbopixels: Fast superpixels using geometric flows, IEEE Transactions
on Pattern Analysis and Machine Intelligence 31 (12) (2009) 2290–2297.
[311] M.-Y. Liu, O. Tuzel, S. Ramalingam, R. Chellappa, Entropy rate superpixel
segmentation, in: IEEE Conference on Computer Vision and Pattern Recog-
nition (CVPR), IEEE, 2011, pp. 2097–2104.
[312] Z. Li, X.-M. Wu, S.-F. Chang, Segmentation using superpixels: A bipartite
graph partitioning approach, in: IEEE Conference on Computer Vision and
Pattern Recognition (CVPR), IEEE, 2012, pp. 789–796.
[313] Z. Li, J. Chen, Superpixel segmentation using linear spectral clustering, in:
IEEE Conference on Computer Vision and Pattern Recognition (CVPR),
IEEE, 2015, pp. 1356–1363.
278
[314] B. Liu, H. Hu, H. Wang, K. Wang, X. Liu, W. Yu, Superpixel-based classifi-
cation with an adaptive number of classes for polarimetric SAR images, IEEE
Transactions on Geoscience and Remote Sensing, 51 (2) (2013) 907–924.
[315] R. Roscher, B. Waske, Superpixel-based classification of hyperspectral data
using sparse representation and conditional random fields, in: IEEE Interna-
tional Geoscience and Remote Sensing Symposium (IGARSS), IEEE, 2014,
pp. 3674–3677.
[316] S. Wang, H. Lu, F. Yang, M.-H. Yang, Superpixel tracking, in: IEEE Interna-
tional Conference on Computer Vision (ICCV), IEEE, 2011, pp. 1323–1330.
[317] W. Wang, R. Nevatia, Robust object tracking using constellation model with
superpixel, in: Asian Conference on Computer Vision (ACCV), Springer, 2012,
pp. 191–204.
[318] X. Zhou, X. Li, T.-J. Chin, D. Suter, Superpixel-driven level set tracking, in:
IEEE International Conference on Image Processing (ICIP), IEEE, 2012, pp.
409–412.
[319] Z. Cai, L. Wen, Z. Lei, N. Vasconcelos, S. Z. Li, Robust deformable and
occluded object tracking with dynamic graph, IEEE Transactions on Image
Processing 23 (12) (2014) 5497–5509.
[320] S. Theodoridis, K. Koutroumbas, Pattern Recognition, 4th Edition, Academic
Press, USA, 2008.
[321] R. E. Kalman, A new approach to linear filtering and prediction problems,
Journal of Basic Engineering 82 (1) (1960) 35–45.
279
[322] P. D. Moral, Non-linear filtering: Interacting particle resolution, Markov Pro-
cesses and Related Fields 2 (4) (1996) 555–581.
[323] J. S. Liu, R. Chen, Sequential Monte Carlo methods for dynamic systems,
Journal of the American Statistical Association 93 (443) (1998) 1032–1044.
[324] M. Isard, A. Blake, CONDENSATION - Conditional density propagation for
visual tracking, International Journal of Computer Vision 29 (1) (1998) 5–28.
[325] Z. Zhu, Q. Ji, K. Fujimura, K. Lee, Combining Kalman filtering and mean shift
for real time eye tracking under active IR illumination, in: IEEE International
Conference on Pattern Recognition (ICPR), Vol. 4, IEEE, 2002, pp. 318–321.
[326] N. Funk, A study of the Kalman filter applied to visual tracking, University
of Alberta, Project for CMPUT 652 (2003) 1–26.
[327] E. V. Cuevas, D. Zaldivar, R. Rojas, Kalman filter for vision tracking, Tech-
nical Report B 05-12, Freie University, Germany (2005).
[328] S.-K. Weng, C.-M. Kuo, S.-K. Tu, Video object tracking using adaptive
Kalman filter, Journal of Visual Communication and Image Representation
17 (6) (2006) 1190–1208.
[329] D. Angelova, L. Mihaylova, Extended object tracking using mixture Kalman
filtering, in: International Conference on Numerical Methods and Applica-
tions, Springer, 2006, pp. 122–130.
[330] Y. Yoon, A. Kosaka, A. C. Kak, A new Kalman-filter-based framework for fast
and accurate visual tracking of rigid objects, IEEE Transactions on Robotics
24 (5) (2008) 1238–1251.
280
[331] X. Li, K. Wang, W. Wang, Y. Li, A multiple object tracking method using
Kalman filter, in: IEEE International Conference on Information and Automa-
tion (ICIA), IEEE, 2010, pp. 1862–1866.
[332] Z. Fu, Y. Han, Centroid weighted Kalman filter for visual object tracking,
Measurement 45 (4) (2012) 650–655.
[333] A. Salhi, A. Y. Jammoussi, Object tracking system using Camshift, meanshift
and Kalman filter, World Academy of Science, Engineering and Technology 64
(2012) 674–679.
[334] M. Isard, A. Blake, Condensation conditional density propagation for visual
tracking, International Journal of Computer Vision 29 (1) (1998) 5–28.
[335] Y. Wu, T. S. Huang, Robust visual tracking by integrating multiple cues based
on co-inference learning, International Journal of Computer Vision 58 (1)
(2004) 55–71.
[336] C. Yang, R. Duraiswami, L. Davis, Fast multiple object tracking via a hierar-
chical particle filter, in: IEEE International Conference on Computer Vision
(ICCV), Vol. 1, IEEE, 2005, pp. 212–219.
[337] Z. Khan, T. Balch, F. Dellaert, A Rao-Blackwellized particle filter for Eigen-
tracking, in: IEEE Conference on Computer Vision and Pattern Recognition
(CVPR), Vol. 2, IEEE, 2004, pp. 974–980.
[338] G. Casella, C. P. Robert, Rao-blackwellisation of sampling schemes,
Biometrika 83 (1) (1996) 81–94.
281
[339] S. K. Zhou, R. Chellappa, B. Moghaddam, Visual tracking and recognition
using appearance-adaptive models in particle filters, IEEE Transactions on
Image Processing 13 (11) (2004) 1491–1506.
[340] P. Brasnett, L. Mihaylova, D. Bull, N. Canagarajah, Sequential Monte Carlo
tracking by fusing multiple cues in video sequences, Image and Vision Com-
puting 25 (8) (2007) 1217–1227.
[341] M. Fotouhi, A. Gholami, S. Kasaei, Particle filter-based object tracking using
adaptive histogram, in: IEEE 7th Iranian Conference on Machine Vision and
Image Processing (MVIP), IEEE, 2011, pp. 1–5.
[342] F. Gustafsson, F. Gunnarsson, N. Bergman, U. Forssell, J. Jansson, R. Karls-
son, P.-J. Nordlund, Particle filters for positioning, navigation, and tracking,
IEEE Transactions on Signal Processing 50 (2) (2002) 425–437.
[343] C. Hue, J. L. Cadre, P. Pérez, Tracking multiple objects with particle filtering,
IEEE Transactions on Aerospace and Electronic Systems 38 (3) (2002) 791–
812.
[344] M. Jaward, L. Mihaylova, N. Canagarajah, D. Bull, Multiple object tracking
using particle filters, in: IEEE Conference on Aerospace, IEEE, 2006, pp. 1–8.
[345] X. Jia, H. Lu, M.-H. Yang, Visual tracking via adaptive structural local sparse
appearance model, in: IEEE Conference on Computer Vision and Pattern
Recognition (CVPR), IEEE, 2012, pp. 1822–1829.
[346] W. Zhong, H. Lu, M.-H. Yang, Robust object tracking via sparse collabora-
tive appearance model, IEEE Transactions on Image Processing 23 (5) (2014)
2356–2368.
282
[347] A. C. Copeland, M. M. Trivedi, Models and metrics for signature strength
evaluation of camouflaged targets, in: AeroSense’97, International Society for
Optics and Photonics, 1997, pp. 194–199.
[348] F. M. Gretzmacher, G. S. Ruppert, S. Nyberg, Camouflage assessment con-
sidering human perception data, in: Aerospace/Defense Sensing and Controls,
International Society for Optics and Photonics, 1998, pp. 58–67.
[349] J. Yu, Z. Cao, Q. Lai, The optimal camouflage pattern assessment and design
in all conditions, Journal of Materials Science Research 4 (3) (2015) 76–97.
[350] A. Toet, M. A. Hogervorst, Urban camouflage assessment through visual search
and computational saliency, Optical Engineering 52 (4) (2013) 041103–041111.
[351] S. K. Singh, C. A. Dhawale, S. Misra, Survey of object detection methods in
camouflaged image, IERI Procedia 4 (2013) 351–357.
[352] H. Du, X. Jin, X. Mao, Digital camouflage images using two-scale decomposi-
tion, in: Computer Graphics Forum, Vol. 31, Wiley Online Library, 2012, pp.
2203–2212.
[353] M. Harville, G. Gordon, J. Woodfill, Foreground segmentation using adaptive
mixture models in color and depth, in: Proceedings on IEEE Workshop on
Detection and Recognition of Events in Video, IEEE, 2001, pp. 3–11.
[354] T. E. Boult, R. J. Micheals, X. Gao, M. Eckmann, Into the woods: Visual
surveillance of noncooperative and camouflaged targets in complex outdoor
settings, Proceedings of the IEEE 89 (10) (2001) 1382–1402.
283
[355] P. KaewTrakulPong, R. Bowden, A real time adaptive visual surveillance sys-
tem for tracking low-resolution colour targets in dynamically changing scenes,
Image and Vision Computing 21 (10) (2003) 913–929.
[356] Z. Q. Huang, Z. Jiang, Tracking camouflaged objects with weighted region
consolidation, in: Proceedings on Digital Image Computing: Techniques and
Applications (DICTA), IEEE, 2005, pp. 24–31.
[357] T. Chandesa, T. Pridmore, A. Bargiela, Detecting occlusion and camouflage
during visual tracking, in: IEEE International Conference on Signal and Image
Processing Applications (ICSIPA), IEEE, 2009, pp. 468–473.
[358] D. Conte, P. Foggia, G. Percannella, F. Tufano, M. Vento, An algorithm for
detection of partially camouflaged people, in: 6th IEEE International Confer-
ence on Advanced Video and Signal Based Surveillance (AVSS), IEEE, 2009,
pp. 340–345.
[359] A. Loza, L. Mihaylova, D. Bull, N. Canagarajah, Structural similarity-based
object tracking in multimodality surveillance videos, Machine Vision and Ap-
plications 20 (2) (2009) 71–83.
[360] J. Y. Y. H. W. Hou, J. Li, Detection of the mobile object with camouflage
color under dynamic background based on optical flow, Procedia Engineering
15 (2011) 2201–2205.
[361] T. Malathi, K. M. Bhuyan, Foreground object detection under camouflage
using multiple camera-based codebooks, in: Annual IEEE India Conference
(INDICON), IEEE, 2013, pp. 1–6.
284
[362] H. T. Nguyen, M. Worring, R. van den Boomgaard, A. Smeulders, Track-
ing non-parameterized object contours in video, IEEE Transactions on Image
Processing 11 (9) (2002) 1081–1091.
[363] N. Paragios, O. Mellina-Gottardo, V. Ramesh, Gradient vector flow fast geo-
metric active contours, IEEE Transactions on Pattern Analysis and Machine
Intelligence 26 (3) (2004) 402–407.
[364] F. Tang, S. Brennan, Q. Zhao, H. Tao, Co-tracking using semi-supervised Sup-
port Vector Machines, in: IEEE 11th International Conference on Computer
Vision (ICCV), IEEE, 2007, pp. 1–8.
[365] A. Baumann, M. Boltz, J. Ebling, M. Koenig, H. S. Loos, M. Merkel, W. Niem,
J. K. Warzelhan, J. Yu, A review and comparison of measures for automatic
video surveillance systems, EURASIP Journal on Image and Video Processing
2008 (1) (2008) 1–30.
[366] R. Kasturi, D. Goldgof, P. Soundararajan, V. Manohar, J. Garofolo, R. Bow-
ers, M. Boonstra, V. Korzhova, J. Zhang, Framework for performance evalua-
tion of face, text, and vehicle detection and tracking in video: Data, metrics
and protocol, IEEE Transactions on Pattern Analysis and Machine Intelligence
31 (2) (2009) 319–336.
[367] N. Lazarevic-McManus, J. R. Renno, D. Makris, G. A. Jones, An object-
based comparative methodology for motion detection based on the F-measure,
Computer Vision and Image Understanding 111 (1) (2008) 74–85.
285
[368] W. Aitfares, E. Bouyakhf, A. Herbulot, F. Regragui, M. Devy, Hybrid region
and interest points-based active contour for object tracking, Applied Mathe-
matical Sciences 7 (118) (2013) 5879–5899.
[369] W. T. Freeman, M. Roth, Orientation histograms for hand gesture recognition,
in: IEEE Internatinal Workshop on Automatic Face and Gesture Recognition,
IEEE, 1995, pp. 296–301.
[370] K. Levi, Y. Weiss, Learning object detection from a small number of examples:
the importance of good features, in: IEEE Conference on Computer Vision and
Pattern Recognition (CVPR), Vol. 2, IEEE, 2004, pp. 53–60.
[371] F. Suard, A. Rakotomamonjy, A. Bensrhair, Pedestrian detection using In-
frared images and histograms of oriented gradients, in: IEEE International
Conference on Intelligent Vehicles (ICIV), IEEE, 2006, pp. 206–212.
[372] J. M. Keller, M. R. Gray, J. A. Givens, A fuzzy K-nearest neighbor algorithm,
IEEE Transactions on Systems, Man, and Cybernetics 15 (4) (1985) 580–585.
[373] D. F. Specht, Probabilistic neural networks, Neural networks 3 (1) (1990)
109–118.
[374] M. T. Musavi, K. H. Chan, D. M. Hummels, K. Kalantri, On the generalization
ability of neural network classifiers, IEEE Transactions on Pattern Analysis
and Machine Intelligence 16 (6) (1994) 659–663.
[375] W. Yan, C. Weber, S. Wermter, A hybrid probabilistic neural model for person
tracking based on a ceiling-mounted camera, Journal of Ambient Intelligence
and Smart Environments 3 (3) (2011) 237–252.
286
[376] W. Hao, B. Zhang, W. Tian, Head tracking by means of probabilistic neural
networks, Measurement Science and Technology 18 (7) (2007) 1999–2009.
[377] H. G. Traven, A neural network approach to statistical pattern classification by
semiparametric estimation of probability density functions, IEEE Transactions
on Neural Networks 2 (3) (1991) 366–377.
[378] K. Z. Mao, K.-C. Tan, W. Ser, Probabilistic neural-network structure determi-
nation for pattern classification, IEEE Transactions on Neural Networks 11 (4)
(2000) 1009–1016.
[379] C. M. Bishop, Neural Networks for Pattern Recognition, 1st Edition, Claren-
don Press, Oxford, United Kingdom, 1995.
[380] S. Krinidis, V. Chatzis, Fuzzy energy-based active contours, IEEE Transactons
on Image Processing 18 (12) (2009) 2747–2755.
[381] Y. Wu, W. Ma, M. Gong, H. Li, L. Jiao, Novel fuzzy active contour model
with kernel metric for image segmentation, Applied Soft Computing 34 (2015)
301–311.
[382] S. Challa, M. R. Morelande, D. Musicki, R. J. Evans, Fundamentals of Ob-
ject Tracking, 1st Edition, Cambridge University Press, Cambridge, United
Kingdom, 2011.
[383] X. Lan, A. J. Ma, P. C. Yuen, Multi-cue visual tracking using robust feature-
level fusion based on joint sparse representation, in: IEEE Conference on
Computer Vision and Pattern Recognition (CVPR), IEEE, 2014, pp. 1194–
1201.
287
[384] T. Ojala, M. Pietikäinen, D. Harwood, A comparative study of texture mea-
sures with classification based on featured distributions, Pattern Recognition
29 (1) (1996) 51–59.
[385] M. A. Akhloufi, A. Bendada, Locally adaptive texture features for multispec-
tral face recognition, in: IEEE International Conference on Systems, Man and
Cybernetics (SMC), IEEE, 2010, pp. 3308–3314.
[386] C. E. Metz, Basic principles of ROC analysis, in: Seminars in Nuclear
Medicine, Vol. 8, Elsevier, 1978, pp. 283–298.
[387] P. Burrascano, Learning vector quantization for the probabilistic neural net-
work, IEEE transactions on Neural Networks 2 (4) (1990) 458–461.
[388] P. Raghu, B. Yegnanarayana, Supervised texture classification using a proba-
bilistic neural network and constraint satisfaction model, IEEE Transactions
on Neural Networks 9 (3) (1998) 516–522.
[389] A. Mondal, S. Ghosh, A. Ghosh, Efficient silhouette-based contour tracking
using local information, Soft Computing 20 (2) (2016) 785–805.
[390] Y. Pan, J. D. Birdwell, S. M. Djouadi, Efficient implementation of the Chan-
Vese models without solving PDEs, in: IEEE Workshop on Multimedia Signal
Processing, IEEE, 2006, pp. 350–354.
[391] L. He, S. Osher, Solving the Chan-Vese model by a multiphase level set al-
gorithm based on the topological derivative, in: International Conference on
Scale Space and Variational Methods in Computer Vision, Springer, 2007, pp.
777–788.
288
[392] C. Li, C.-Y. Kao, J. C. Gore, Z. Ding, Minimization of region-scalable fit-
ting energy for image segmentation, IEEE Transactions on Image Processing
17 (10) (2008) 1940–1949.
[393] S. Osher, J. A. Sethian, Fronts propagating with curvature dependent speed:
Algorithms based on Hamilton-Jacobi formulation, Journal of Computational
Physics 79 (1) (1988) 12–49.
[394] C. W. Fox, An Introduction to the Calculus of Variations, 1st Edition, Oxford
University Press, New York, United Kingdom, 1988.
[395] B. Song, T. Chan, A fast algorithm for level set based optimization, UCLA
Cam Report 68 (2002) 2–68.
[396] L. Sevilla-Lara, E. Learned-Miller, Distribution fields for tracking, in: IEEE
Conference on Computer Vision and Pattern Recognition (CVPR), IEEE,
2012, pp. 1910–1917.
[397] J. F. Henriques, R. Caseiro, P. Martins, J. Batista, High-speed tracking with
kernelized correlation filters, IEEE Transactions on Pattern Analysis and Ma-
chine Intelligence 37 (3) (2015) 583–596.
[398] Y. Wu, J. Lim, M.-H. Yang, Online object tracking: A benchmark, in: IEEE
Conference on Computer Vision and Pattern Recognition (CVPR), IEEE,
2013, pp. 2411–2418.
[399] C. Desai, D. Ramanan, C. C. Fowlkes, Discriminative models for multi-class
object layout, International Journal of Computer Vision 95 (1) (2011) 1–12.
[400] M. Yang, Y. Wu, G. Hua, Context-aware visual tracking, IEEE Transactions
on Pattern Analysis and Machine Intelligence 31 (7) (2009) 1195–1209.
289
[401] J. Kwon, K. M. Lee, Visual tracking decomposition, in: IEEE Conference on
Computer Vision and Pattern Recognition (CVPR), IEEE, 2010, pp. 1269–
1276.
[402] J. Kwon, K. M. Lee, Tracking by sampling trackers, in: IEEE International
Conference on Computer Vision (ICCV), IEEE, 2011, pp. 1195–1202.
[403] S. He, Q. Yang, R. Lau, J. Wang, M.-H. Yang, Visual tracking via locality
sensitive histograms, in: IEEE Conference on Computer Vision and Pattern
Recognition (CVPR), IEEE, 2013, pp. 2427–2434.
[404] C. Leistner, M. Godec, A. Saffari, H. Bischof, On-line multi-view forests for
tracking, in: IEEE International Conference on Pattern Recognition (ICPR),
2010, pp. 493–502.
[405] N. Jiang, W. Liu, Y. Wu, Adaptive and discriminative metric differential
tracking, in: IEEE Conference on Computer Vision and Pattern Recognition
(CVPR), IEEE, 2011, pp. 1161–1168.
[406] Z. Kalal, K. Mikolajczyk, J. Matas, Tracking-learning-detection, IEEE Trans-
actions on Pattern Analysis and Machine Intelligence 34 (7) (2012) 1409–1422.
[407] M. Danelljan, G. Häger, F. Khan, M. Felsberg, Accurate scale estimation for
robust visual tracking, in: British Machine Vision Conference (BMVA), BMVA
Press, 2014, pp. 1–11.
[408] A. W. Smeulders, D. M. Chu, R. Cucchiara, S. Calderara, A. Dehghan,
M. Shah, Visual tracking: An experimental survey, IEEE Transactions on
Pattern Analysis and Machine Intelligence 36 (7) (2014) 1442–1468.
290
[409] L. I. Kuncheva, Combining Pattern Classifiers: Methods and Algorithms, 1st
Edition, John Wiley & Sons, Hoboken, New Jersey, 2004.
[410] S. Haykin, Neural Networks A Comprehensive Foundation, 1st Edition, Pren-
tice Hall, Inc., New Jersey, U.S.A, 1999.
[411] H. Kurokawa, C.-Y. Ho, S. Mori, A novel back propagation algorithm with
optimal number of hidden units, in: International Conference on Artifical
Neural Network, Springer, 1993, pp. 783–783.
[412] R. Benmokhtar, B. Huet, Neural network combining classifier based on
Dempster-Shafer theory for semantic indexing in video content, in: Advances
in Multimedia Modeling, Springer, 2007, pp. 196–205.
[413] D.-S. Lee, S. N. Srihari, A theory of classifier combination: the neural net-
work approach, in: 3rd International Conference on Document Analysis and
Recognition (DAR), Vol. 1, IEEE, 1995, pp. 42–45.
[414] S. X. Liao, M. Pawlak, On image analysis by moments, IEEE Transactions on
Pattern Analysis and Machine Intelligence 18 (3) (1996) 254–266.
[415] R. Mukundan, K. R. Ramakrishnan, Moment Functions in Image Analysis:
Theory and Applications, 1st Edition, World Scientific, Singapore, 1998.
[416] J. Zhang, S. Ma, S. Sclaroff, MEEM: robust tracking via multiple experts using
entropy minimization, in: European Conference on Computer Vision (ECCV),
Springer, 2014, pp. 188–203.
[417] L. Wang, W. Ouyang, X. Wang, H. Lu, Visual tracking with fully convolutional
networks, in: Proceedings of the IEEE International Conference on Computer
Vision (ICCV), IEEE, 2015, pp. 3119–3127.
291
[418] O. Mazhelis, One-class classifiers: A review and analysis of suitability in the
context of mobile-masquerader detection, South African Computer Journal
36 (36) (2006) 29–48.
[419] S. S. Khan, M. G. Madden, A survey of recent trends in one class classification,
in: Artificial Intelligence and Cognitive Science, Springer, 2009, pp. 188–197.
[420] S. S. Khan, M. G. Madden, One-class classification: Taxonomy of study and
review of techniques, The Knowledge Engineering Review 29 (3) (2014) 345–
374.
292

Mais conteúdo relacionado

Semelhante a 19_bibliography.pdf

Unleashing the Potentials of Immersive Augmented Reality for Software Enginee...
Unleashing the Potentials of Immersive Augmented Reality for Software Enginee...Unleashing the Potentials of Immersive Augmented Reality for Software Enginee...
Unleashing the Potentials of Immersive Augmented Reality for Software Enginee...Leonel Merino
 
New Research Articles 2019 July Issue International Journal of Artificial Int...
New Research Articles 2019 July Issue International Journal of Artificial Int...New Research Articles 2019 July Issue International Journal of Artificial Int...
New Research Articles 2019 July Issue International Journal of Artificial Int...gerogepatton
 
Desney S Tan Curriculum Vitae
Desney S Tan Curriculum VitaeDesney S Tan Curriculum Vitae
Desney S Tan Curriculum Vitaebutest
 
Desney S Tan Curriculum Vitae
Desney S Tan Curriculum VitaeDesney S Tan Curriculum Vitae
Desney S Tan Curriculum Vitaebutest
 
Top 10 cited Computer Networks & Communications Research Articles From 2017 I...
Top 10 cited Computer Networks & Communications Research Articles From 2017 I...Top 10 cited Computer Networks & Communications Research Articles From 2017 I...
Top 10 cited Computer Networks & Communications Research Articles From 2017 I...IJCNCJournal
 
Estimating Number of People in ITU-EEB as an Application of People Counting T...
Estimating Number of People in ITU-EEB as an Application of People Counting T...Estimating Number of People in ITU-EEB as an Application of People Counting T...
Estimating Number of People in ITU-EEB as an Application of People Counting T...Fellowship at Vodafone FutureLab
 
Trans-Disciplinary Practice week 6
Trans-Disciplinary Practice week 6Trans-Disciplinary Practice week 6
Trans-Disciplinary Practice week 6R. Sosa
 
September 2021 - Top 10 Read Articles in Signal & Image Processing
September 2021 - Top 10 Read Articles in Signal & Image ProcessingSeptember 2021 - Top 10 Read Articles in Signal & Image Processing
September 2021 - Top 10 Read Articles in Signal & Image Processingsipij
 
TOP 10 STORAGE & RETRIEVAL PAPERS : RECOMMENDED READING
TOP 10 STORAGE & RETRIEVAL PAPERS :  RECOMMENDED READINGTOP 10 STORAGE & RETRIEVAL PAPERS :  RECOMMENDED READING
TOP 10 STORAGE & RETRIEVAL PAPERS : RECOMMENDED READINGsipij
 
June 2021: Top Read Articles in Signal & Image Processing
June 2021: Top Read Articles in Signal & Image ProcessingJune 2021: Top Read Articles in Signal & Image Processing
June 2021: Top Read Articles in Signal & Image Processingsipij
 
New Research Articles 2019 September Issue International Journal of Artificia...
New Research Articles 2019 September Issue International Journal of Artificia...New Research Articles 2019 September Issue International Journal of Artificia...
New Research Articles 2019 September Issue International Journal of Artificia...gerogepatton
 
November 2021: Top Read Articles in Signal & Image Processing
November 2021: Top Read Articles in Signal & Image ProcessingNovember 2021: Top Read Articles in Signal & Image Processing
November 2021: Top Read Articles in Signal & Image Processingsipij
 
December 2021: Top Read Articles in Signal & Image Processing
December 2021: Top Read Articles in Signal & Image ProcessingDecember 2021: Top Read Articles in Signal & Image Processing
December 2021: Top Read Articles in Signal & Image Processingsipij
 
October 2023-Top Cited Articles in IJU.pdf
October 2023-Top Cited Articles in IJU.pdfOctober 2023-Top Cited Articles in IJU.pdf
October 2023-Top Cited Articles in IJU.pdfijujournal
 
July 2021: Top Read Articles in Signal & Image Processing
July 2021: Top Read Articles in Signal & Image ProcessingJuly 2021: Top Read Articles in Signal & Image Processing
July 2021: Top Read Articles in Signal & Image Processingsipij
 
Elegant Resume
Elegant ResumeElegant Resume
Elegant Resumebutest
 
Elegant Resume
Elegant ResumeElegant Resume
Elegant Resumebutest
 
Feature Extraction of Gesture Recognition Based on Image Analysis for Differe...
Feature Extraction of Gesture Recognition Based on Image Analysis for Differe...Feature Extraction of Gesture Recognition Based on Image Analysis for Differe...
Feature Extraction of Gesture Recognition Based on Image Analysis for Differe...IJERA Editor
 
Feature Extraction of Gesture Recognition Based on Image Analysis for Differe...
Feature Extraction of Gesture Recognition Based on Image Analysis for Differe...Feature Extraction of Gesture Recognition Based on Image Analysis for Differe...
Feature Extraction of Gesture Recognition Based on Image Analysis for Differe...IJERA Editor
 

Semelhante a 19_bibliography.pdf (20)

Unleashing the Potentials of Immersive Augmented Reality for Software Enginee...
Unleashing the Potentials of Immersive Augmented Reality for Software Enginee...Unleashing the Potentials of Immersive Augmented Reality for Software Enginee...
Unleashing the Potentials of Immersive Augmented Reality for Software Enginee...
 
New Research Articles 2019 July Issue International Journal of Artificial Int...
New Research Articles 2019 July Issue International Journal of Artificial Int...New Research Articles 2019 July Issue International Journal of Artificial Int...
New Research Articles 2019 July Issue International Journal of Artificial Int...
 
Desney S Tan Curriculum Vitae
Desney S Tan Curriculum VitaeDesney S Tan Curriculum Vitae
Desney S Tan Curriculum Vitae
 
Desney S Tan Curriculum Vitae
Desney S Tan Curriculum VitaeDesney S Tan Curriculum Vitae
Desney S Tan Curriculum Vitae
 
Top 10 cited Computer Networks & Communications Research Articles From 2017 I...
Top 10 cited Computer Networks & Communications Research Articles From 2017 I...Top 10 cited Computer Networks & Communications Research Articles From 2017 I...
Top 10 cited Computer Networks & Communications Research Articles From 2017 I...
 
Estimating Number of People in ITU-EEB as an Application of People Counting T...
Estimating Number of People in ITU-EEB as an Application of People Counting T...Estimating Number of People in ITU-EEB as an Application of People Counting T...
Estimating Number of People in ITU-EEB as an Application of People Counting T...
 
Trans-Disciplinary Practice week 6
Trans-Disciplinary Practice week 6Trans-Disciplinary Practice week 6
Trans-Disciplinary Practice week 6
 
September 2021 - Top 10 Read Articles in Signal & Image Processing
September 2021 - Top 10 Read Articles in Signal & Image ProcessingSeptember 2021 - Top 10 Read Articles in Signal & Image Processing
September 2021 - Top 10 Read Articles in Signal & Image Processing
 
TOP 10 STORAGE & RETRIEVAL PAPERS : RECOMMENDED READING
TOP 10 STORAGE & RETRIEVAL PAPERS :  RECOMMENDED READINGTOP 10 STORAGE & RETRIEVAL PAPERS :  RECOMMENDED READING
TOP 10 STORAGE & RETRIEVAL PAPERS : RECOMMENDED READING
 
June 2021: Top Read Articles in Signal & Image Processing
June 2021: Top Read Articles in Signal & Image ProcessingJune 2021: Top Read Articles in Signal & Image Processing
June 2021: Top Read Articles in Signal & Image Processing
 
New Research Articles 2019 September Issue International Journal of Artificia...
New Research Articles 2019 September Issue International Journal of Artificia...New Research Articles 2019 September Issue International Journal of Artificia...
New Research Articles 2019 September Issue International Journal of Artificia...
 
November 2021: Top Read Articles in Signal & Image Processing
November 2021: Top Read Articles in Signal & Image ProcessingNovember 2021: Top Read Articles in Signal & Image Processing
November 2021: Top Read Articles in Signal & Image Processing
 
December 2021: Top Read Articles in Signal & Image Processing
December 2021: Top Read Articles in Signal & Image ProcessingDecember 2021: Top Read Articles in Signal & Image Processing
December 2021: Top Read Articles in Signal & Image Processing
 
Blurclassification
BlurclassificationBlurclassification
Blurclassification
 
October 2023-Top Cited Articles in IJU.pdf
October 2023-Top Cited Articles in IJU.pdfOctober 2023-Top Cited Articles in IJU.pdf
October 2023-Top Cited Articles in IJU.pdf
 
July 2021: Top Read Articles in Signal & Image Processing
July 2021: Top Read Articles in Signal & Image ProcessingJuly 2021: Top Read Articles in Signal & Image Processing
July 2021: Top Read Articles in Signal & Image Processing
 
Elegant Resume
Elegant ResumeElegant Resume
Elegant Resume
 
Elegant Resume
Elegant ResumeElegant Resume
Elegant Resume
 
Feature Extraction of Gesture Recognition Based on Image Analysis for Differe...
Feature Extraction of Gesture Recognition Based on Image Analysis for Differe...Feature Extraction of Gesture Recognition Based on Image Analysis for Differe...
Feature Extraction of Gesture Recognition Based on Image Analysis for Differe...
 
Feature Extraction of Gesture Recognition Based on Image Analysis for Differe...
Feature Extraction of Gesture Recognition Based on Image Analysis for Differe...Feature Extraction of Gesture Recognition Based on Image Analysis for Differe...
Feature Extraction of Gesture Recognition Based on Image Analysis for Differe...
 

Último

Vip Mumbai Call Girls Mira Road Call On 9920725232 With Body to body massage ...
Vip Mumbai Call Girls Mira Road Call On 9920725232 With Body to body massage ...Vip Mumbai Call Girls Mira Road Call On 9920725232 With Body to body massage ...
Vip Mumbai Call Girls Mira Road Call On 9920725232 With Body to body massage ...amitlee9823
 
Call Girls Kadugodi Just Call 👗 7737669865 👗 Top Class Call Girl Service Bang...
Call Girls Kadugodi Just Call 👗 7737669865 👗 Top Class Call Girl Service Bang...Call Girls Kadugodi Just Call 👗 7737669865 👗 Top Class Call Girl Service Bang...
Call Girls Kadugodi Just Call 👗 7737669865 👗 Top Class Call Girl Service Bang...amitlee9823
 
Delhi Call Girls Vikaspuri 9711199171 ☎✔👌✔ Whatsapp Hard And Sexy Vip Call
Delhi Call Girls Vikaspuri 9711199171 ☎✔👌✔ Whatsapp Hard And Sexy Vip CallDelhi Call Girls Vikaspuri 9711199171 ☎✔👌✔ Whatsapp Hard And Sexy Vip Call
Delhi Call Girls Vikaspuri 9711199171 ☎✔👌✔ Whatsapp Hard And Sexy Vip Callshivangimorya083
 
John deere 425 445 455 Maitenance Manual
John deere 425 445 455 Maitenance ManualJohn deere 425 445 455 Maitenance Manual
John deere 425 445 455 Maitenance ManualExcavator
 
如何办理女王大学毕业证(QU毕业证书)成绩单原版一比一
如何办理女王大学毕业证(QU毕业证书)成绩单原版一比一如何办理女王大学毕业证(QU毕业证书)成绩单原版一比一
如何办理女王大学毕业证(QU毕业证书)成绩单原版一比一opyff
 
Greenery-Palette Pitch Deck by Slidesgo.pptx
Greenery-Palette Pitch Deck by Slidesgo.pptxGreenery-Palette Pitch Deck by Slidesgo.pptx
Greenery-Palette Pitch Deck by Slidesgo.pptxzohiiimughal286
 
Tata_Nexon_brochure tata nexon brochure tata
Tata_Nexon_brochure tata nexon brochure tataTata_Nexon_brochure tata nexon brochure tata
Tata_Nexon_brochure tata nexon brochure tataaritradey27234
 
ENJOY Call Girls In Okhla Vihar Delhi Call 9654467111
ENJOY Call Girls In Okhla Vihar Delhi Call 9654467111ENJOY Call Girls In Okhla Vihar Delhi Call 9654467111
ENJOY Call Girls In Okhla Vihar Delhi Call 9654467111Sapana Sha
 
John Deere 7430 7530 Tractors Diagnostic Service Manual W.pdf
John Deere 7430 7530 Tractors Diagnostic Service Manual W.pdfJohn Deere 7430 7530 Tractors Diagnostic Service Manual W.pdf
John Deere 7430 7530 Tractors Diagnostic Service Manual W.pdfExcavator
 
Business Bay Escorts $#$ O56521286O $#$ Escort Service In Business Bay Dubai
Business Bay Escorts $#$ O56521286O $#$ Escort Service In Business Bay DubaiBusiness Bay Escorts $#$ O56521286O $#$ Escort Service In Business Bay Dubai
Business Bay Escorts $#$ O56521286O $#$ Escort Service In Business Bay DubaiAroojKhan71
 
Top Rated Call Girls Mumbai Central : 9920725232 We offer Beautiful and sexy ...
Top Rated Call Girls Mumbai Central : 9920725232 We offer Beautiful and sexy ...Top Rated Call Girls Mumbai Central : 9920725232 We offer Beautiful and sexy ...
Top Rated Call Girls Mumbai Central : 9920725232 We offer Beautiful and sexy ...amitlee9823
 
9990611130 Find & Book Russian Call Girls In Vijay Nagar
9990611130 Find & Book Russian Call Girls In Vijay Nagar9990611130 Find & Book Russian Call Girls In Vijay Nagar
9990611130 Find & Book Russian Call Girls In Vijay NagarGenuineGirls
 
Delhi Call Girls Saket 9711199171 ☎✔👌✔ Whatsapp Hard And Sexy Vip Call
Delhi Call Girls Saket 9711199171 ☎✔👌✔ Whatsapp Hard And Sexy Vip CallDelhi Call Girls Saket 9711199171 ☎✔👌✔ Whatsapp Hard And Sexy Vip Call
Delhi Call Girls Saket 9711199171 ☎✔👌✔ Whatsapp Hard And Sexy Vip Callshivangimorya083
 
Why Won't Your Subaru Key Come Out Of The Ignition Find Out Here!
Why Won't Your Subaru Key Come Out Of The Ignition Find Out Here!Why Won't Your Subaru Key Come Out Of The Ignition Find Out Here!
Why Won't Your Subaru Key Come Out Of The Ignition Find Out Here!AutoScandia
 
John Deere Tractors 6130M 6140M Diagnostic Manual
John Deere Tractors  6130M 6140M Diagnostic ManualJohn Deere Tractors  6130M 6140M Diagnostic Manual
John Deere Tractors 6130M 6140M Diagnostic ManualExcavator
 
What Causes BMW Chassis Stabilization Malfunction Warning To Appear
What Causes BMW Chassis Stabilization Malfunction Warning To AppearWhat Causes BMW Chassis Stabilization Malfunction Warning To Appear
What Causes BMW Chassis Stabilization Malfunction Warning To AppearJCL Automotive
 

Último (20)

Vip Mumbai Call Girls Mira Road Call On 9920725232 With Body to body massage ...
Vip Mumbai Call Girls Mira Road Call On 9920725232 With Body to body massage ...Vip Mumbai Call Girls Mira Road Call On 9920725232 With Body to body massage ...
Vip Mumbai Call Girls Mira Road Call On 9920725232 With Body to body massage ...
 
(ISHITA) Call Girls Service Jammu Call Now 8617697112 Jammu Escorts 24x7
(ISHITA) Call Girls Service Jammu Call Now 8617697112 Jammu Escorts 24x7(ISHITA) Call Girls Service Jammu Call Now 8617697112 Jammu Escorts 24x7
(ISHITA) Call Girls Service Jammu Call Now 8617697112 Jammu Escorts 24x7
 
Call Girls Kadugodi Just Call 👗 7737669865 👗 Top Class Call Girl Service Bang...
Call Girls Kadugodi Just Call 👗 7737669865 👗 Top Class Call Girl Service Bang...Call Girls Kadugodi Just Call 👗 7737669865 👗 Top Class Call Girl Service Bang...
Call Girls Kadugodi Just Call 👗 7737669865 👗 Top Class Call Girl Service Bang...
 
Delhi Call Girls Vikaspuri 9711199171 ☎✔👌✔ Whatsapp Hard And Sexy Vip Call
Delhi Call Girls Vikaspuri 9711199171 ☎✔👌✔ Whatsapp Hard And Sexy Vip CallDelhi Call Girls Vikaspuri 9711199171 ☎✔👌✔ Whatsapp Hard And Sexy Vip Call
Delhi Call Girls Vikaspuri 9711199171 ☎✔👌✔ Whatsapp Hard And Sexy Vip Call
 
John deere 425 445 455 Maitenance Manual
John deere 425 445 455 Maitenance ManualJohn deere 425 445 455 Maitenance Manual
John deere 425 445 455 Maitenance Manual
 
Call Girls in Shri Niwas Puri Delhi 💯Call Us 🔝9953056974🔝
Call Girls in  Shri Niwas Puri  Delhi 💯Call Us 🔝9953056974🔝Call Girls in  Shri Niwas Puri  Delhi 💯Call Us 🔝9953056974🔝
Call Girls in Shri Niwas Puri Delhi 💯Call Us 🔝9953056974🔝
 
Stay Cool and Compliant: Know Your Window Tint Laws Before You Tint
Stay Cool and Compliant: Know Your Window Tint Laws Before You TintStay Cool and Compliant: Know Your Window Tint Laws Before You Tint
Stay Cool and Compliant: Know Your Window Tint Laws Before You Tint
 
如何办理女王大学毕业证(QU毕业证书)成绩单原版一比一
如何办理女王大学毕业证(QU毕业证书)成绩单原版一比一如何办理女王大学毕业证(QU毕业证书)成绩单原版一比一
如何办理女王大学毕业证(QU毕业证书)成绩单原版一比一
 
Greenery-Palette Pitch Deck by Slidesgo.pptx
Greenery-Palette Pitch Deck by Slidesgo.pptxGreenery-Palette Pitch Deck by Slidesgo.pptx
Greenery-Palette Pitch Deck by Slidesgo.pptx
 
Tata_Nexon_brochure tata nexon brochure tata
Tata_Nexon_brochure tata nexon brochure tataTata_Nexon_brochure tata nexon brochure tata
Tata_Nexon_brochure tata nexon brochure tata
 
ENJOY Call Girls In Okhla Vihar Delhi Call 9654467111
ENJOY Call Girls In Okhla Vihar Delhi Call 9654467111ENJOY Call Girls In Okhla Vihar Delhi Call 9654467111
ENJOY Call Girls In Okhla Vihar Delhi Call 9654467111
 
John Deere 7430 7530 Tractors Diagnostic Service Manual W.pdf
John Deere 7430 7530 Tractors Diagnostic Service Manual W.pdfJohn Deere 7430 7530 Tractors Diagnostic Service Manual W.pdf
John Deere 7430 7530 Tractors Diagnostic Service Manual W.pdf
 
Business Bay Escorts $#$ O56521286O $#$ Escort Service In Business Bay Dubai
Business Bay Escorts $#$ O56521286O $#$ Escort Service In Business Bay DubaiBusiness Bay Escorts $#$ O56521286O $#$ Escort Service In Business Bay Dubai
Business Bay Escorts $#$ O56521286O $#$ Escort Service In Business Bay Dubai
 
Top Rated Call Girls Mumbai Central : 9920725232 We offer Beautiful and sexy ...
Top Rated Call Girls Mumbai Central : 9920725232 We offer Beautiful and sexy ...Top Rated Call Girls Mumbai Central : 9920725232 We offer Beautiful and sexy ...
Top Rated Call Girls Mumbai Central : 9920725232 We offer Beautiful and sexy ...
 
Call Now ≽ 9953056974 ≼🔝 Call Girls In Shankar vihar ≼🔝 Delhi door step delev...
Call Now ≽ 9953056974 ≼🔝 Call Girls In Shankar vihar ≼🔝 Delhi door step delev...Call Now ≽ 9953056974 ≼🔝 Call Girls In Shankar vihar ≼🔝 Delhi door step delev...
Call Now ≽ 9953056974 ≼🔝 Call Girls In Shankar vihar ≼🔝 Delhi door step delev...
 
9990611130 Find & Book Russian Call Girls In Vijay Nagar
9990611130 Find & Book Russian Call Girls In Vijay Nagar9990611130 Find & Book Russian Call Girls In Vijay Nagar
9990611130 Find & Book Russian Call Girls In Vijay Nagar
 
Delhi Call Girls Saket 9711199171 ☎✔👌✔ Whatsapp Hard And Sexy Vip Call
Delhi Call Girls Saket 9711199171 ☎✔👌✔ Whatsapp Hard And Sexy Vip CallDelhi Call Girls Saket 9711199171 ☎✔👌✔ Whatsapp Hard And Sexy Vip Call
Delhi Call Girls Saket 9711199171 ☎✔👌✔ Whatsapp Hard And Sexy Vip Call
 
Why Won't Your Subaru Key Come Out Of The Ignition Find Out Here!
Why Won't Your Subaru Key Come Out Of The Ignition Find Out Here!Why Won't Your Subaru Key Come Out Of The Ignition Find Out Here!
Why Won't Your Subaru Key Come Out Of The Ignition Find Out Here!
 
John Deere Tractors 6130M 6140M Diagnostic Manual
John Deere Tractors  6130M 6140M Diagnostic ManualJohn Deere Tractors  6130M 6140M Diagnostic Manual
John Deere Tractors 6130M 6140M Diagnostic Manual
 
What Causes BMW Chassis Stabilization Malfunction Warning To Appear
What Causes BMW Chassis Stabilization Malfunction Warning To AppearWhat Causes BMW Chassis Stabilization Malfunction Warning To Appear
What Causes BMW Chassis Stabilization Malfunction Warning To Appear
 

19_bibliography.pdf

  • 1. Bibliography [1] F. Yang, H. Lu, M.-H. Yang, Robust superpixel tracking, IEEE Transactions on Image Processing 23 (4) (2014) 1639–1651. [2] S. Oron, A. Bar-Hillel, D. Levi, S. Avidan, Locally orderless tracking, Inter- national Journal of Computer Vision 111 (2) (2015) 213–228. [3] D. H. Ballard, C. M. Brown, Computer Vision, 1st Edition, Prentice-Hall, New Jersey, 1982. [4] A. Blake, A. Zisserman, Visual Reconstruction, 1st Edition, MIT Press, Lon- don, 1987. [5] M. M. Trivedi, A. Rosenfeld, On making computers see, IEEE Transactions on Systems, Man and Cybernetics 19 (6) (1989) 1333–1335. [6] R. Jain, R. Kasturi, B. G. Schunck, Machine Vision, 1st Edition, McGraw-Hill, New Delhi, 1995. [7] G. M. Petersen, Range-finding in the army. How to use range-finders to get results: The erect and inverted types, Popular Science Monthly 96 (1919) 118–120. 239
  • 2. [8] R. Sim, J. J. Little, Autonomous vision-based exploration and mapping us- ing hybrid maps and Rao-Blackwellised particle filters, in: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, 2006, pp. 2082–2089. [9] A. L. Bovic, Handbook of Image and Video Processing, 1st Edition, Academic Press, New York, 2000. [10] X. Li, W. Hu, C. Shen, Z. Zhang, A. Dick, A. V. D. Hengel, A survey of appearance models in visual object tracking, ACM Transactions on Intelligent Systems and Technology (TIST) 4 (4) (2013) 1–58. [11] A. M. Tekalp, Digital Video Processing, 1st Edition, Prentice Hall, New Jersey, 1995. [12] I. Haritaoglu, D. Harwood, L. S. Davis, W4: Real-time surveillance of peo- ple and their activities, IEEE Transactions on Pattern Analysis and Machine Intelligence 22 (8) (2000) 809–830. [13] R. T. Collins, A. J. Lipton, T. Kanade, H. Fujiyoshi, D. Duggins, Y. Tsin, D. Tolliver, N. Enomoto, O. Hasegawa, P. Burt, et al., A system for video surveillance and monitoring, Technical Report CMU-RI-TR-00-12, Carnegie Mellon University, Pittsburg (2000). [14] J. A. Quinn, R. Nakibuule, Traffic flow monitoring in crowded cities., in: AAAI Spring Symposium: Artificial Intelligence for Development, AAAI, 2010, pp. 73–78. 240
  • 3. [15] S. Kamijo, Y. Matsushita, K. Ikeuchi, M. Sakauchi, Traffic monitoring and accident detection at intersections, IEEE Transactions on Intelligent Trans- portation Systems 1 (2) (2000) 108–118. [16] J.-C. Tai, S.-T. Tseng, C.-P. Lin, K.-T. Song, Real-time image tracking for automatic traffic monitoring and enforcement applications, Image and Vision Computing 22 (6) (2004) 485–501. [17] A. B. Chan, Z.-S. J. Liang, N. Vasconcelos, Privacy preserving crowd monitor- ing: Counting people without people models or tracking, in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2008, pp. 1–7. [18] M. Hashemzadeh, G. Pan, M. Yao, Counting moving people in crowds using motion statistics of feature-points, Multimedia Tools and Applications 72 (1) (2014) 453–487. [19] S. Lenser, M. Veloso, Visual sonar: Fast obstacle avoidance using monocular vision, in: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vol. 1, IEEE, 2003, pp. 886–891. [20] C. Song, H. Zhao, W. Jing, Y. Bi, Robust video stabilization based on bounded path planning, in: IEEE International Conference on Pattern Recognition (ICPR), IEEE, 2012, pp. 3684–3687. [21] F.-S. Chen, C.-M. Fu, C.-L. Huang, Hand gesture recognition using a real-time tracking method and hidden Markov models, Image and Vision Computing 21 (8) (2003) 745–758. 241
  • 4. [22] N. D. Binh, E. Shuichi, T. Ejima, Real-time hand tracking and gesture recogni- tion system, in: Proceding of Graphics Vision and Image Processing (GVIP), Citeseer, 2005, pp. 19–21. [23] S. S. Ge, Y. Yang, T. H. Lee, Hand gesture recognition and tracking based on distributed locally linear embedding, Image and Vision Computing 26 (12) (2008) 1607–1620. [24] S. Paschalakis, M. Bober, Real-time face detection and tracking for mobile videoconferencing, Real-Time Imaging 10 (2) (2004) 81–94. [25] E. Bardinet, L. D. Cohen, N. Ayache, Tracking and motion analysis of the left ventricle with deformable superquadrics, Medical Image Analysis 1 (2) (1996) 129–149. [26] E. Kochavi, D. Goldsher, H. Azhari, Method for rapid MRI needle tracking, Magnetic Resonance in Medicine 51 (5) (2004) 1083–1087. [27] P. Mountney, G.-Z. Yang, Soft tissue tracking for minimally invasive surgery: Learning local deformation online, in: Medical Image Computing and Computer-Assisted Intervention (MICCAI), Springer, 2008, pp. 364–372. [28] W. Geng, P. Cosman, C. C. Berry, Z. Feng, W. R. Schafer, Automatic tracking, feature extraction and classification of C. elegans phenotypes, IEEE Transac- tions on Biomedical Engineering 51 (10) (2004) 1811–1820. [29] A. Veeraraghavan, R. Chellappa, M. Srinivasan, Shape-and-behavior encoded tracking of Bee dances, IEEE Transactions on Pattern Analysis and Machine Intelligence 30 (3) (2008) 463–476. 242
  • 5. [30] E. Sahouria, A. Zakhor, A trajectory based video indexing system for street surveillance, in: IEEE International Conference on Image Processing (ICIP), IEEE, 1999, pp. 24–28. [31] J. S. Yuk, K.-Y. K. Wong, R. H. Chung, K. Chow, F. Y. Chin, K. S. Tsang, Object-based surveillance video retrieval system with real-time index- ing methodology, in: Image Analysis and Recognition, Springer, 2007, pp. 626–637. [32] D. A. Forsyth, J. Ponce, Computer Vision: A Modern Approach, 1st Edition, Prentice Hall, New Jersey, 2003. [33] R. M. Haralick, L. G. Shapiro, Computer and Robot Vision, 1st Edition, Addison-Wesley Publishing Company, New York, 1992. [34] B. K. P. Horn, Robot Vision, 1st Edition, MIT Press, Cambridge, USA, 1986. [35] A. Yilmaz, O. Javed, M. Shah, Object tracking: A survey, ACM Computing Surveys 38 (4) (2006) 1–45. [36] E. Maggio, A. Cavallaro, Video Tracking: Theory and Practice, 1st Edition, John Wiley & Sons, United Kingdom, 2011. [37] B. D. Lucas, T. Kanade, et al., An iterative image registration technique with an application to stereo vision., in: Proceedings of International Joint Conference in Artificial Intelligence (IJCAI), Vol. 81, IEEE, 1981, pp. 674– 679. [38] J. Shi, C. Tomasi, Good features to track, in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 1994, pp. 593–600. 243
  • 6. [39] C. J. Veenman, M. J. Reinders, E. Backer, Resolving motion correspondence for densely moving points, IEEE Transactions on Pattern Analysis and Ma- chine Intelligence 23 (1) (2001) 54–72. [40] V. Lepetit, P. Lagger, P. Fua, Randomized trees for real-time keypoint recog- nition, in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Vol. 2, IEEE, 2005, pp. 775–781. [41] T. Rémi, M. Bernard, Probabilistic matching algorithm for keypoint based object tracking using a delaunay triangulation, in: International Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS), IEEE, 2007, pp. 1–17. [42] T. T. H. Tran, E. Marchand, Real-time keypoints matching: application to vi- sual servoing, in: IEEE International Conference on Robotics and Automation (ICRA), IEEE, 2007, pp. 3787–3792. [43] G. Nebehay, R. Pflugfelder, TLM: tracking-learning-matching of keypoints, in: IEEE International Conference on Distributed Smart Cameras (ICDSC), IEEE, 2013, pp. 1–6. [44] B. Babenko, M.-H. Yang, S. Belongie, Robust object tracking with online mul- tiple instance learning, IEEE Transactions on Pattern Analysis and Machine Intelligence 33 (8) (2011) 1619–1632. [45] J. Dou, Q. Qin, Z. Tu, Improved weighted multiple instance learning for object tracking, Optik-International Journal for Light and Electron Optics 126 (24) (2015) 5287–5293. 244
  • 7. [46] G. R. Bradski, Computer vision face tracking for use in a perceptual user interface, Intel Technology Journal Q2 2 (1998) 1–15. [47] D. Comaniciu, V. Ramesh, P. Meer, Kernel-based object tracking, IEEE Trans- actions on Pattern Analysis and Machine Intelligence 25 (5) (2003) 564–577. [48] X. An, J. Kim, Y. Han, Optimal colour-based mean shift algorithm for tracking objects, IET Computer Vision 8 (3) (2014) 235–244. [49] L. Vacchetti, V. Lepetit, P. Fua, Stable real-time 3D tracking using online and offline information, IEEE Transactions on Pattern Analysis and Machine Intelligence 26 (10) (2004) 1385–1391. [50] J. Giebel, D. M. Gavrila, C. Schnörr, A Bayesian framework for multi-cue 3D object tracking, in: European Conference on Computer Vision (ECCV), Springer, 2004, pp. 241–252. [51] Y. Park, V. Lepetit, W. Woo, Multiple 3D object tracking for augmented reality, in: Proceedings of IEEE/ACM International Symposium on Mixed and Augmented Reality, IEEE, 2008, pp. 117–120. [52] L. Wang, W. Hu, T. Tan, Recent developments in human motion analysis, Pattern Recognition 36 (3) (2003) 585–601. [53] A. Sundaresan, R. Chellappa, Multicamera tracking of articulated human mo- tion using shape and motion cues, IEEE Transactions on Image Processing, 18 (9) (2009) 2114–2126. [54] L. Mussi, S. Ivekovic, S. Cagnoni, Markerless articulated human body tracking from multi-view video with GPU-PSO, in: Evolvable Systems: from Biology to Hardware, Springer, 2010, pp. 97–108. 245
  • 8. [55] I. Oikonomidis, N. Kyriazis, A. A. Argyros, Tracking the articulated motion of two strongly interacting hands, in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2012, pp. 1862–1869. [56] A. Ali, J. Aggarwal, Segmentation and recognition of continuous human ac- tivity, in: Proceedings of IEEE Workshop on Detection and Recognition of Events in Video (DREV), IEEE, 2001, pp. 28–35. [57] A. Kar, Skeletal tracking using Microsoft kinect, Methodology 1 (2010) 1–11. [58] L. A. Schwarz, A. Mkhitaryan, D. Mateus, N. Navab, Human skeleton tracking from depth data using geodesic distances and optical flow, Image and Vision Computing 30 (3) (2012) 217–226. [59] N. R. Howe, Silhouette lookup for automatic pose tracking, in: IEEE Con- ference on Computer Vision and Pattern Recognition Workshop (CVPRW), IEEE, 2004, pp. 15–22. [60] B. Rosenhahn, U. Kersting, S. Andrew, T. Brox, R. Klette, H.-P. Seidel, A silhouette based human motion tracking system, Tectnical Report 1530, CITR, University of Auckland, New Zealand (2005). [61] A. Yilmaz, X. Li, M. Shah, Object contour tracking using level sets, in: Asian Conference on Computer Vision (ACCV), Vol. 1, Springer, 2004, pp. 1–7. [62] M. Yokoyama, T. Poggio, A contour-based moving object detection and track- ing, in: Joint IEEE International Workshop on Visual Surveillance and Per- formance Evaluation of Tracking and Surveillance (WVSPETS), IEEE, 2005, pp. 271–276. 246
  • 9. [63] T. Brox, A. Bruhn, N. Papenberg, J. Weickert, High accuracy optical flow esti- mation based on a theory for warping, in: European Conference on Computer Vision (ECCV), Springer, 2004, pp. 25–36. [64] P. Sand, S. Teller, Particle video: Long-range motion estimation using point trajectories, International Journal of Computer Vision 80 (1) (2008) 72–91. [65] S. Salti, A. Cavallaro, L. D. Stefano, Adaptive appearance modeling for video tracking: Survey and evaluation, IEEE Transactions on Image Processing 21 (10) (2012) 4334–4348. [66] G. Silveira, E. Malis, Real-time visual tracking under arbitrary illumination changes, in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Vol. 1, IEEE, 2007, pp. 1–6. [67] J. Ho, K.-C. Lee, M.-H. Yang, D. Kriegman, Visual tracking using learned lin- ear subspaces, in: IEEE Conference on Computer Vision and Pattern Recog- nition (CVPR), Vol. 1, IEEE, 2004, pp. 782–789. [68] Y. Li, On incremental and robust subspace learning, Pattern Recognition 37 (7) (2004) 1509–1518. [69] D. A. Ross, J. Lim, R.-S. Lin, M.-H. Yang, Incremental learning for robust visual tracking, International Journal of Computer Vision 77 (13) (2008) 125– 141. [70] S. Baker, I. Matthews, Lucas-Kanade 20 years on: A unifying framework, International Journal of Computer Vision 56 (3) (2004) 221–255. 247
  • 10. [71] H. T. Nguyen, A. W. Smeulders, Fast occluded object tracking by a robust appearance filter, IEEE Transactions on Pattern Analysis and Machine Intel- ligence 26 (8) (2004) 1099–1104. [72] X. Li, W. Hu, Z. Zhang, X. Zhang, G. Luo, Robust visual tracking based on incremental tensor subspace learning, in: IEEE 11th International Conference on Computer Vision (ICCV), IEEE, 2007, pp. 1–8. [73] T. Wang, I. Y. Gu, P. Shi, Object tracking using incremental 2D-PCA learning and ML estimation, in: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Vol. 1, IEEE, 2007, pp. 933–936. [74] X. Li, W. Hu, Z. Zhang, X. Zhang, M. Zhu, J. Cheng, Visual tracking via in- cremental log-Euclidean Riemannian subspace learning, in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2008, pp. 1–8. [75] J. Wen, X. Li, X. Gao, D. Tao, Incremental learning of weighted tensor sub- space for visual tracking, in: IEEE International Conference on Systems, Man and Cybernetics (SMC), IEEE, 2009, pp. 3688–3693. [76] W. Hu, X. Li, X. Zhang, X. Shi, S. Maybank, Z. Zhang, Incremental tensor subspace learning and its applications to foreground segmentation and track- ing, International Journal of Computer Vision 91 (3) (2011) 303–327. [77] M. S. Allili, D. Ziou, Object of interest segmentation and tracking by using feature selection and active contours, in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2007, pp. 1–8. 248
  • 11. [78] M. Werlberger, W. Trobin, T. Pock, A. Wedel, D. Cremers, H. Bischof, Anisotropic Huber-L1 optical flow, in: British Machine Vision Conference (BMVC), Vol. 1, BMVA Press, 2009, pp. 1–11. [79] Y. Wu, J. Fan, Contextual flow, in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2009, pp. 33–40. [80] J. Santner, C. Leistner, A. Saffari, T. Pock, H. Bischof, PROST: parallel robust online simple tracking, in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2010, pp. 723–730. [81] Q. Zhao, Z. Yang, H. Tao, Differential Earth Mover’s distance with its applica- tions to visual tracking, IEEE Transactions on Pattern Analysis and Machine Intelligence 32 (2) (2010) 274–287. [82] B. Georgescu, P. Meer, Point matching under large image deformations and illumination changes, IEEE Transactions on Pattern Analysis and Machine Intelligence 26 (6) (2004) 674–688. [83] C. Yang, R. Duraiswami, L. Davis, Efficient mean-shift tracking via a new similarity measure, in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Vol. 1, IEEE, 2005, pp. 176–183. [84] S. T. Birchfield, S. Rangarajan, Spatiograms versus histograms for region- based tracking, in: IEEE Conference on Computer Vision and Pattern Recog- nition (CVPR), Vol. 2, IEEE, 2005, pp. 1158–1163. [85] S. T. Birchfield, S. Rangarajan, Spatial histograms for region-based tracking, Electronics and Telecommunications Research Institute (ETRI) Journal 29 (5) (2007) 697–699. 249
  • 12. [86] B. R. Venkatesh, A. Makur, Kernel-based spatial-color modeling for fast mov- ing object tracking, in: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Vol. 1, IEEE, 2007, pp. 901–904. [87] D.-H. Kim, H.-K. Kim, S.-J. Ko, et al., Spatial color histogram based center voting method for subsequent object tracking and segmentation, Image and Vision Computing 29 (12) (2011) 850–860. [88] A. Adam, E. Rivlin, I. Shimshoni, Robust fragments-based tracking using the integral histogram, in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Vol. 1, IEEE, 2006, pp. 798–805. [89] S. S. Nejhum, J. Ho, M.-H. Yang, Online visual tracking with histograms and articulating blocks, Computer Vision and Image Understanding 114 (8) (2010) 901–914. [90] W. Zhong, H. Lu, M.-H. Yang, Robust object tracking via sparsity-based collaborative model, in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2012, pp. 1838–1845. [91] V. Takala, M. Pietikainen, Multi-object tracking using color, texture and motion, in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2007, pp. 1–7. [92] J. Ning, L. Zhang, D. Zhang, C. Wu, Robust object tracking using joint color- texture histogram, International Journal of Pattern Recognition and Artificial Intelligence 23 (7) (2009) 1245–1263. 250
  • 13. [93] M. Diwakar, P. K. Patel, K. Gupta, C. Chauhan, Object tracking using joint enhanced color-texture histogram, in: IEEE 2nd International Conference on Image Information Processing (ICIIP), IEEE, 2013, pp. 160–165. [94] S. Birchfield, Elliptical head tracking using intensity gradients and color his- tograms, in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 1998, pp. 232–237. [95] I. Haritaoglu, M. Flickner, Detection and tracking of shopping groups in stores, in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Vol. 1, IEEE, 2001, pp. 431–438. [96] J. Wang, Y. Yagi, Integrating color and shape-texture features for adap- tive real-time object tracking, IEEE Transactions on Image Processing 17 (2) (2008) 235–240. [97] A. Gelzinis, A. Verikas, M. Bacauskiene, Increasing the discrimination power of the co-occurrence matrix-based features, Pattern Recognition 40 (9) (2007) 2367–2372. [98] R. M. Haralick, K. Shanmugam, I. H. Dinstein, Textural features for im- age classification, IEEE Transactions on Systems, Man and Cybernetics 3 (6) (1973) 610–621. [99] F. Porikli, O. Tuzel, P. Meer, Covariance tracking using model update based on Lie Algebra, in: IEEE Conference on Computer Vision and Pattern Recog- nition (CVPR), Vol. 1, IEEE, 2006, pp. 728–735. 251
  • 14. [100] O. Tuzel, F. Porikli, P. Meer, Region covariance: A fast descriptor for detection and classification, in: European Conference on Computer Vision (ECCV), Springer, 2006, pp. 589–600. [101] G. Li, D. Liang, Q. Huang, S. Jiang, W. Gao, Object tracking using incre- mental 2D-LDA learning and Bayes inference, in: IEEE 15th International Conference on Image Processing (ICIP), IEEE, 2008, pp. 1568–1571. [102] Y. Wu, J. Cheng, J. Wang, H. Lu, Real-time visual tracking via incremental covariance tensor learning, in: IEEE 12th International Conference on Com- puter Vision (CVPR), IEEE, 2009, pp. 1631–1638. [103] X. Hong, H. Chang, S. Shan, B. Zhong, X. Chen, W. Gao, Sigma set based implicit online learning for object tracking, IEEE Signal Processing Letters 17 (9) (2010) 807–810. [104] I. Austvoll, B. Kwolek, Region covariance matrix-based object tracking with occlusions handling, in: Computer Vision and Graphics, Springer, 2010, pp. 201–208. [105] W. Hu, X. Li, W. Luo, X. Zhang, S. Maybank, Z. Zhang, Single and multiple object tracking using log-Euclidean Riemannian subspace and block-division appearance model, IEEE Transactions on Pattern Analysis and Machine In- telligence 34 (12) (2012) 2420–2440. [106] Y. Wu, J. Cheng, J. Wang, H. Lu, J. Wang, H. Ling, E. Blasch, L. Bai, Real-time probabilistic covariance tracking with efficient model update, IEEE Transactions on Image Processing 21 (5) (2012) 2824–2837. 252
  • 15. [107] C. He, Y. F. Zheng, S. C. Ahalt, Object tracking using the Gabor wavelet transform and the golden section algorithm, IEEE Transactions on Multimedia 4 (4) (2002) 528–538. [108] A. Mojaev, A. Zell, Image decomposition and tracking with Gabor wavelets, Machine Intelligence and Robotics Control 1 (1) (2003) 3–9. [109] A. Khare, U. S. Tiwary, Daubechies complex wavelet transform based moving object tracking, in: IEEE Symposium on Computational Intelligence in Image and Signal Processing (CIISP), IEEE, 2007, pp. 36–40. [110] M. Li, Z. Zhang, K. Huang, T. Tan, Robust visual tracking based on simpli- fied biologically inspired features, in: IEEE 16th International Conference on Image Processing (ICIP), IEEE, 2009, pp. 4113–4116. [111] O. Prakash, A. Khare, Tracking of non-rigid object in complex wavelet domain, Journal of Signal and Information Processing 2 (2) (2011) 105–111. [112] X. Li, A. Dick, C. Shen, D. H. A. Van, H. Wang, Incremental learning of 3D- DCT compact representations for robust visual tracking, IEEE Transactions on Pattern Analysis and Machine Intelligence 35 (4) (2013) 863–881. [113] L. Yu, X. Zhang, L. Zheng, A new object tracking algorithm based on the fast discrete curvelet transform, International Journal of Signal Processing, Image Processing and Pattern Recognition 7 (1) (2014) 53–64. [114] N. Paragios, R. Deriche, Geodesic active contours and level sets for the detec- tion and tracking of moving objects, IEEE Transactions on Pattern Analysis and Machine Intelligence 22 (3) (2000) 266–280. 253
  • 16. [115] D. Cremers, Dynamical statistical shape priors for level set-based tracking, IEEE Transactions on Pattern Analysis and Machine Intelligence 28 (8) (2006) 1262–1273. [116] M. S. Allili, D. Ziou, Object of interest segmentation and tracking by using feature selection and active contours, in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2007, pp. 1–8. [117] N. Vaswani, Y. Rathi, A. Yezzi, A. Tannenbaum, PF-MT with an interpolation effective basis for tracking local contour deformations, IEEE Transactions on Image Processing 19 (4) (2008) 841–857. [118] D.-X. Lai, Y.-H. Chang, Z.-H. Zhong, Active contour tracking of moving ob- jects using edge flows and Ant colony optimization in video sequences, in: Advances in Image and Video Technology, Springer, 2009, pp. 1104–1116. [119] C.-H. Chuang, Y.-L. Chao, Z.-P. Li, Moving object segmentation and tracking using active contour and color classification models, in: IEEE International Symposium on Multimedia (ISM), IEEE, 2010, pp. 73–80. [120] X. Sun, H. Yao, S. Zhang, A novel supervised level set method for non-rigid object tracking, in: IEEE Conference on Computer Vision and Pattern Recog- nition (CVPR), IEEE, 2011, pp. 3393–3400. [121] X. Mei, H. Ling, Robust visual tracking and vehicle classification via sparse representation, IEEE Transactions on Pattern Analysis and Machine Intelli- gence 33 (11) (2011) 2259–2272. 254
  • 17. [122] F. Chen, Q. Wang, S. Wang, W. Zhang, W. Xu, Object tracking via appear- ance modeling and sparse representation, Image and Vision Computing 29 (11) (2011) 787–796. [123] Z. Han, J. Jiao, B. Zhang, Q. Ye, J. Liu, Visual object tracking via sample- based adaptive sparse representation (AdaSR), Pattern Recognition 44 (9) (2011) 2170–2183. [124] T. Bai, Y. F. Li, Robust visual tracking with structured sparse representation appearance model, Pattern Recognition 45 (6) (2012) 2390–2404. [125] Q. Wang, F. Chen, W. Xu, M.-H. Yang, Online discriminative object track- ing with local sparse representation, in: IEEE Workshop on Applications of Computer Vision (WACV), IEEE, 2012, pp. 425–432. [126] T. Zhang, B. Ghanem, S. Liu, N. Ahuja, Low-rank sparse learning for ro- bust visual tracking, in: European Conference on Computer Vision (ECCV), Springer, 2012, pp. 470–484. [127] T. Zhang, B. Ghanem, S. Liu, N. Ahuja, Robust visual tracking via structured multi-task sparse learning, International Journal of Computer Vision 101 (2) (2013) 367–383. [128] Y. Bai, M. Tang, Object tracking via robust multitask sparse representation, IEEE Signal Processing Letters 21 (8) (2014) 909–913. [129] D. G. Lowe, Distinctive image features from scale-invariant keypoints, Inter- national Journal of Computer Vision 60 (2) (2004) 91–110. 255
  • 18. [130] F. Tang, H. Tao, Probabilistic object tracking with dynamic attributed rela- tional feature graph, IEEE Transactions on Circuits and Systems for Video Technology 18 (8) (2008) 1064–1074. [131] H. Zhou, Y. Yuan, C. Shi, Object tracking using SIFT features and mean shift, Computer Vision and Image Understanding 113 (3) (2009) 345–352. [132] Y. Yan, J. Wang, C. Li, Z. Wu, Object tracking using SIFT features in a parti- cle filter, in: IEEE 3rd International Conference on Communication Software and Networks (ICCSN), IEEE, 2011, pp. 384–388. [133] S.-W. Ha, Y.-H. Moon, Multiple object tracking using SIFT features and lo- cation matching, International Journal of Smart Home 5 (4) (2011) 17–26. [134] H. Bay, T. Tuytelaars, G. L. Van, SURF: Speeded up robust features, in: European Conference on Computer vision (ECCV), Springer, 2006, pp. 404– 417. [135] H. Bay, T. Tuytelaars, G. L. Van, Speeded-Up Robust Features (SURF), Com- puter Vision and Image Understanding 110 (3) (2008) 346–359. [136] W. He, T. Yamashita, H. Lu, S. Lao, SURF tracking, in: IEEE 12th Interna- tional Conference on Computer Vision (ICCV), IEEE, 2009, pp. 1586–1592. [137] D.-N. Ta, W.-C. Chen, N. Gelfand, K. Pulli, SURFTrac: Efficient tracking and continuous object recognition using local feature descriptors, in: IEEE Con- ference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2009, pp. 2937–2944. 256
  • 19. [138] J. Zhang, J. Fang, J. Lu, Mean-shift algorithm integrating with SURF for tracking, in: IEEE 7th International Conference on Natural Computation (ICNC), Vol. 2, IEEE, 2011, pp. 960–963. [139] H. Shuo, W. Na, S. Huajun, Object tracking method based on SURF, AASRI Procedia 3 (2012) 351–356. [140] Z. Zhou, X. Ou, J. Xu, SURF feature detection method used in object track- ing, in: IEEE International Conference on Machine Learning and Cybernetics (ICMLC), Vol. 4, IEEE, 2013, pp. 1865–1868. [141] J. Sivic, F. Schaffalitzky, A. Zisserman, Object level grouping for video shots, in: European Conference on Computer Vision (ECCV), Springer, 2004, pp. 85–98. [142] J. Sivic, F. Schaffalitzky, A. Zisserman, Object level grouping for video shots, International Journal of Computer Vision 67 (2) (2006) 189–210. [143] M. Donoser, H. Bischof, Efficient Maximally Stable Extremal Region (MSER) tracking, in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Vol. 1, IEEE, 2006, pp. 553–560. [144] S. Tran, L. Davis, Robust object trackinng with regional affine invariant fea- tures, in: IEEE 11th International Conference on Computer Vision (ICCV), IEEE, 2007, pp. 1–8. [145] P. Tissainayagam, D. Suter, Object tracking in image sequences using point features, Pattern Recognition 38 (1) (2005) 105–113. 257
  • 20. [146] M. Grabner, H. Grabner, H. Bischof, Learning features for tracking, in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2007, pp. 1–8. [147] N. Li, L. Liu, D. Xu, Corner feature based object tracking using adaptive Kalman filter, in: IEEE 9th International Conference on Signal Processing (ICSP), IEEE, 2008, pp. 1432–1435. [148] Z. Kim, Real time object tracking based on dynamic feature grouping with background subtraction, in: IEEE Conference on Computer Vision and Pat- tern Recognition (CVPR), IEEE, 2008, pp. 1–8. [149] S. E. Palmer, Vision Science: Photons to Phenomenology, 1st Edition, MIT Press, London, 1999. [150] J. M. Wolfe, Guided search 2.0 A revised model of visual search, Psychonomic Bulletin & Review 1 (2) (1994) 202–238. [151] S. Li, M. C. Lee, Fast visual tracking using motion saliency in video, in: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Vol. 1, IEEE, 2007, pp. 1073–1076. [152] S. Zhang, F. Stentiford, A saliency based object tracking method, in: IEEE In- ternational Workshop on Content-Based Multimedia Indexing (CBMI), IEEE, 2008, pp. 512–517. [153] G. Zhang, Z. Yuan, N. Zheng, X. Sheng, T. Liu, Visual saliency based object tracking, in: Asian Conference on Computer Vision (ACCV), Springer, 2009, pp. 193–203. 258
  • 21. [154] D. Sidibé, D. Fofi, F. Mériaudeau, Using visual saliency for object tracking with particle filters, in: 18th IEEE European Conference on Signal Processing (ECSP), IEEE, 2010, pp. 1776–1780. [155] V. Mahadevan, N. Vasconcelos, Biologically inspired object tracking using center-surround saliency mechanisms, IEEE Transactions on Pattern Analysis and Machine Intelligence 35 (3) (2013) 541–554. [156] D. Zhang, W. Li, M. Sun, H. Yu, Saliency map for object tracking, Interna- tional Journal of Signal Processing, Image Processing and Pattern Recognition 8 (10) (2015) 233–240. [157] S. Hong, T. You, S. Kwak, B. Han, Online tracking by learning dis- criminative saliency map with convolutional neural network, arXiv preprint arXiv:1502.06796 (2015) 1–10. [158] X. Ren, J. Malik, Tracking as repeated figure/ground segmentation, in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2007, pp. 1–8. [159] Z. Yin, R. T. Collins, Shape constrained figure-ground segmentation and tracking, in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2009, pp. 731–738. [160] F. Li, T. Kim, A. Humayun, D. Tsai, J. Rehg, Video segmentation by tracking many figure-ground segments, in: IEEE International Conference on Computer Vision (ICCV), IEEE, 2013, pp. 2192–2199. 259
  • 22. [161] R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, S. Susstrunk, SLIC super- pixels compared to state-of-the-art superpixel methods, IEEE Transactions on Pattern Analysis and Machine Intelligence 34 (11) (2012) 2274–2282. [162] S. Wang, H. Lu, F. Yang, M.-H. Yang, Superpixel tracking, in: IEEE Interna- tional Conference on Computer Vision (ICCV), IEEE, 2011, pp. 1323–1330. [163] W. Wang, R. Nevatia, Robust object tracking using constellation model with superpixel, in: Asian Conference on Computer Vision (ACCV), Springer, 2012, pp. 191–204. [164] Z. Cai, L. Wen, Z. Lei, N. Vasconcelos, S. Z. Li, Robust deformable and occluded object tracking with dynamic graph, IEEE Transactions on Image Processing 23 (12) (2014) 5497–5509. [165] Z. Lin, L. S. Davis, D. Doermann, D. DeMenthon, Hierarchical part-template matching for human detection and segmentation, in: IEEE 11th International Conference on Computer Vision (ICCV), IEEE, 2007, pp. 1–8. [166] F. Pernici, B. A. Del, Object tracking by oversampling local features, IEEE Transactions on Pattern Analysis and Machine Intelligence 36 (12) (2014) 2538–2551. [167] T. Ojala, M. Pietikäinen, T. Mäenpää, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns, IEEE Transactions on Pattern Analysis and Machine Intelligence 24 (7) (2002) 971–987. [168] X. Tan, B. Triggs, Enhanced local texture feature sets for face recognition under difficult lighting conditions, IEEE Transactions on Image Processing 19 (6) (2010) 1635–1650. 260
  • 23. [169] N. Dalal, B. Triggs, Histograms of oriented gradients for human detection, in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Vol. 1, IEEE, 2005, pp. 886–893. [170] H. T. Nguyen, A. Smeulders, Tracking aspects of the foreground against the background, in: European Conference on Computer Vision (ECCV), Springer, 2004, pp. 446–456. [171] S. Avidan, Support vector tracking, IEEE Transactions on Pattern Analysis and Machine Intelligence 26 (8) (2004) 1064–1072. [172] H. Grabner, H. Bischof, On-line boosting and vision, in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Vol. 1, IEEE, 2006, pp. 260–267. [173] H. Grabner, M. Grabner, H. Bischof, Real-time tracking via on-line boosting, in: Proceedings of British Machine Vision Conference (BMVC), Vol. 1, BMVA Press, 2006, pp. 1–10. [174] Y. Freund, R. Schapire, N. Abe, A short introduction to boosting, Journal- Japanese Society for Artificial Intelligence 14 (5) (1999) 771–780. [175] X. Liu, T. Yu, Gradient feature selection for online boosting, in: IEEE 11th International Conference on Computer Vision (ICCV), IEEE, 2007, pp. 1–8. [176] S. Avidan, Ensemble tracking, IEEE Transactions on Pattern Analysis and Machine Intelligence 29 (2) (2007) 261–271. [177] T. Parag, F. Porikli, A. Elgammal, Boosting adaptive linear weak classifiers for online learning and tracking, in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2008, pp. 1–8. 261
  • 24. [178] I. Visentini, L. Snidaro, G. L. Foresti, Dynamic ensemble for target tracking, in: 8th International Workshop on Visual Surveillance (VS), 2008, pp. 1–8. [179] C. Leistner, A. Saffari, P. M. Roth, H. Bischof, On robustness of on-line boosting-A competitive study, in: IEEE 12th International Conference on Computer Vision Workshops (ICCVW), IEEE, 2009, pp. 1362–1369. [180] A. Blum, T. Mitchell, Combining labeled and unlabeled data with co-training, in: Proceedings of 11th Annual Conference on Computational Learning The- ory, ACM, 1998, pp. 92–100. [181] X. Zhu, Semi-supervised learning literature survey, Tectnical Report 1530, University of Wisconsin, Madison (2007). [182] O. Chapelle, B. Scholkopf, A. Zien, Semi-Supervised Learning, 1st Edition, MIT Press, Cambridge, 2006. [183] H. Grabner, C. Leistner, H. Bischof, Semi-supervised on-line boosting for robust tracking, in: European Conference on Computer Vision (ECCV), Springer, 2008, pp. 234–247. [184] R. Liu, J. Cheng, H. Lu, A robust boosting tracker with minimum error bound in a co-training framework., in: IEEE 12th International Conference on Com- puter Vision (ICCV), IEEE, 2009, pp. 1459–1466. [185] K. Zhang, H. Song, Real-time visual tracking via online weighted multiple instance learning, Pattern Recognition 46 (1) (2013) 397–411. [186] M. Li, J. T. Kwok, B.-L. Lu, Online multiple instance learning with no regret, in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2010, pp. 1395–1401. 262
  • 25. [187] L. J. Wang, H. Zhang, Visual tracking based on an improved online multi- ple instance learning algorithm, Computational Intelligence and Neuroscience 2016 (2015) 1–9. [188] C. Xu, W. Tao, Z. Meng, Z. Feng, Robust visual tracking via online multiple instance learning with Fisher information, Pattern Recognition 48 (12) (2015) 3917–3926. [189] B. Zeisl, C. Leistner, A. Saffari, H. Bischof, On-line semi-supervised multiple- instance boosting, in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2010, pp. 1879–1879. [190] G. Li, Q. Huang, L. Qin, S. Jiang, SSOCBT: A robust semi-supervised online CovBoost tracker that uses samples differently, IEEE Transactions on Circuits and Systems for Video Technology 23 (4) (2013) 695–709. [191] S. J. Pan, Q. Yang, A survey on transfer learning, IEEE Transactions on Knowledge and Data Engineering 22 (10) (2010) 1345–1359. [192] W. Luo, X. Li, W. Li, W. Hu, Robust visual tracking via transfer learning, in: IEEE 18th International Conference on Image Processing (ICIP), IEEE, 2011, pp. 485–488. [193] C. Gao, N. Sang, R. Huang, Online transfer boosting for object tracking, in: IEEE 21st International Conference on Pattern Recognition (ICPR), IEEE, 2012, pp. 906–909. [194] Q. Wang, F. Chen, J. Yang, W. Xu, M.-H. Yang, Transferring visual prior for online object tracking, IEEE Transactions on Image Processing 21 (7) (2012) 3296–3305. 263
  • 26. [195] Z. Dan, N. Sang, R. Huang, S. Sun, Instance transfer boosting for object tracking, Optik-International Journal for Light and Electron Optics 124 (18) (2013) 3446–3450. [196] J. Gao, H. Ling, W. Hu, J. Xing, Transfer learning based visual tracking with Gaussian processes regression, in: European Conference on Computer Vision (ECCV), Springer, 2014, pp. 188–203. [197] N. Wang, S. Li, A. Gupta, D.-Y. Yeung, Transferring rich feature hierarchies for robust visual tracking, arXiv preprint arXiv:1501.04587 (2015) 1–9. [198] L. Breiman, Random forests, Machine Learning 45 (1) (2001) 5–32. [199] V. Lepetit, P. Fua, Keypoint recognition using randomized trees, IEEE Trans- actions on Pattern Analysis and Machine Intelligence 28 (9) (2006) 1465–1479. [200] J. Shotton, M. Johnson, R. Cipolla, Semantic texton forests for image cate- gorization and segmentation, in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2008, pp. 1–8. [201] M. Özuysal, M. Calonder, V. Lepetit, P. Fua, Fast keypoint recognition using random ferns, IEEE Transactions on Pattern Analysis and Machine Intelli- gence 32 (3) (2010) 448–461. [202] A. Saffari, C. Leistner, J. Santner, M. Godec, H. Bischof, On-line random forests, in: IEEE 12th International Conference on Computer Vision Work- shops (ICCVW), IEEE, 2009, pp. 1393–1400. [203] M. Godec, C. Leistner, A. Saffari, H. Bischof, On-line random Naive Bayes for tracking, in: IEEE 20th International Conference on Pattern Recognition (ICPR), IEEE, 2010, pp. 3545–3548. 264
  • 27. [204] C. Leistner, A. Saffari, H. Bischof, MIForests: Multiple-instance learning with randomized trees, in: European Conference on Computer Vision (ECCV), Springer, 2010, pp. 29–42. [205] X. Shi, X. Zhang, Y. Liu, W. Hu, H. Ling, Multi-cue based multi-target track- ing using online random forests, in: IEEE International Conference on Acous- tics, Speech and Signal Processing (ICASSP), IEEE, 2011, pp. 1185–1188. [206] J. Gall, A. Yao, N. Razavi, G. L. Van, V. Lempitsky, Hough forests for object detection, tracking, and action recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence 33 (11) (2011) 2188–2202. [207] C. Rao, C. Yao, X. Bai, W. Qiu, W. Liu, Online random ferns for robust vi- sual tracking, in: IEEE 21st International Conference on Pattern Recognition (ICPR), IEEE, 2012, pp. 1447–1450. [208] P. Deng, L. Zhou, B. Wang, Visual tracking based on local patches and ferns forest, in: IEEE 12th International Conference on Signal Processing (ICSP), IEEE, 2014, pp. 760–763. [209] R.-S. Lin, M.-H. Yang, S. E. Levinson, Object tracking using incremental Fisher discriminant analysis, in: Proceedings of the IEEE 17th International Conference on Pattern Recognition (ICPR), Vol. 2, IEEE, 2004, pp. 757–760. [210] Z. Xu, P. Shi, X. Xu, Adaptive subclass discriminant analysis color space learn- ing for visual tracking, in: Pacific-Rim Conference on Multimedia, Springer, 2008, pp. 902–905. 265
  • 28. [211] J. Wen, X. Gao, Y. Yuan, D. Tao, J. Li, Incremental tensor biased discriminant analysis: A new color-based visual tracking method, Neurocomputing 73 (4) (2010) 827–839. [212] X. Wang, G. Hua, T. X. Han, Discriminative tracking by metric learning, in: European Conference on Computer Vision (ECCV), Springer, 2010, pp. 200–214. [213] N. Jiang, W. Liu, H. Su, Y. Wu, Tracking low resolution objects by metric preservation, in: IEEE Conference on Computer Vision and Pattern Recogni- tion (CVPR), IEEE, 2011, pp. 1329–1336. [214] Y. Cong, J. Yuan, Y. Tang, Object tracking via online metric learning, in: IEEE 19th International Conference on Image Processing (ICIP), IEEE, 2012, pp. 417–420. [215] N. Jiang, W. Liu, Y. Wu, Order determination and sparsity-regularized metric learning adaptive visual tracking, in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2012, pp. 1956–1963. [216] C. Gao, F. Chen, J.-G. Yu, R. Huang, N. Sang, Exemplar-based linear discrim- inant analysis for robust object tracking, in: IEEE International Conference on Image Processing (ICIP), IEEE, 2014, pp. 388–392. [217] A. Krizhevsky, I. Sutskever, G. E. Hinton, Imagenet classification with deep convolutional neural networks, in: Advances in Neural Information Processing Systems (NIPS), NIPS, 2012, pp. 1097–1105. [218] G. Hinton, L. Deng, D. Yu, G. E. Dahl, A.-R. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. N. Sainath, et al., Deep neural networks for 266
  • 29. acoustic modeling in speech recognition: The shared views of four research groups, IEEE Signal Processing Magazine 29 (6) (2012) 82–97. [219] N. Wang, D.-Y. Yeung, Learning a deep compact image representation for vi- sual tracking, in: Advances in Neural Information Processing Systems (NIPS), NIPS, 2013, pp. 809–817. [220] J. Jin, A. Dundar, J. Bates, C. Farabet, E. Culurciello, Tracking with deep neural networks, in: IEEE 47th Annual Conference on Information Sciences and Systems (CISS), IEEE, 2013, pp. 1–5. [221] H. Li, Y. Li, F. Porikli, et al., Deeptrack: Learning discriminative feature rep- resentations by convolutional neural networks for visual tracking., in: British Machine Vision Conference (BMVC), Vol. 1, BMVA Press, 2014, pp. 1–11. [222] C. Ma, J.-B. Huang, X. Yang, M.-H. Yang, Hierarchical convolutional features for visual tracking, in: Proceedings of the IEEE International Conference on Computer Vision (ICCV), IEEE, 2015, pp. 3074–3082. [223] D. Hu, X. Zhou, J. Wu, Visual tracking based on convolutional deep belief network, in: Advanced Parallel Processing Technologies, Springer, 2015, pp. 103–115. [224] P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, P.-A. Manzagol, Stacked de- noising autoencoders: Learning useful representations in a deep network with a local denoising criterion, The Journal of Machine Learning Research 11 (2010) 3371–3408. [225] F. Yang, H. Lu, Y.-W. Chen, Bag of features tracking, in: IEEE 20th Interna- tional Conference on Pattern Recognition (ICPR), IEEE, 2010, pp. 153–156. 267
  • 30. [226] J. Gall, N. Razavi, L. Van Gool, On-line adaption of class-specific codebooks for instance tracking, in: Proceedings of the British Machine Vision Conference (BMVC), BMVA Press, 2010, pp. 1–12. [227] Q. Zhong, Z. Qingqing, G. Tengfei, Moving object tracking based on codebook and particle filter, Procedia Engineering 29 (2012) 174–178. [228] F. Yang, H.-H. Lu, W. Zhang, G.-M. Yang, Visual tracking via bag of features, IET Image Processing 6 (2) (2012) 115–128. [229] F. Yang, H. Lu, M.-H. Yang, Learning structured visual dictionary for object tracking, Image and Vision Computing 31 (12) (2013) 992–999. [230] T. Ren, Z. Qiu, Y. Liu, T. Yu, J. Bei, Soft-assigned bag of features for object tracking, Multimedia Systems 21 (2) (2015) 189–205. [231] S. Hare, A. Saffari, P. H. Torr, Struck: Structured output tracking with kernels, in: IEEE International Conference on Computer Vision (ICCV), IEEE, 2011, pp. 263–270. [232] R. Yao, Q. Shi, C. Shen, Y. Zhang, A. van den Hengel, Robust tracking with weighted online structured learning, in: European Conference on Computer Vision (ECCV), Springer, 2012, pp. 158–172. [233] R. Yao, Q. Shi, C. Shen, Y. Zhang, A. Hengel, Part-based visual tracking with online latent structural learning, in: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2013, pp. 2363– 2370. 268
  • 31. [234] Y. Bai, M. Tang, Robust visual tracking via ranking SVM, in: IEEE 18th International Conference on Image Processing (ICIP), IEEE, 2011, pp. 517– 520. [235] Y. Bai, M. Tang, Robust tracking via weakly supervised ranking SVM, in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2012, pp. 1854–1861. [236] S. J. McKenna, Y. Raja, S. Gong, Tracking colour objects using adaptive mixture models, Image and Vision Computing 17 (3) (1999) 225–231. [237] C. Stauffer, W. E. L. Grimson, Adaptive background mixture models for real- time tracking, in: IEEE Conference on Computer Vision and Pattern Recog- nition (CVPR), Vol. 2, IEEE, 1999, pp. 246–252. [238] C. Stauffer, W. E. L. Grimson, Learning patterns of activity using real-time tracking, IEEE Transactions on Pattern Analysis and Machine Intelligence 22 (8) (2000) 747–757. [239] P. KaewTraKulPong, R. Bowden, An improved adaptive background mixture model for real-time tracking with shadow detection, in: Video-based Surveil- lance Systems, Springer, 2002, pp. 135–144. [240] B. Han, L. Davis, On-line density-based appearance modeling for object track- ing, in: IEEE 10th International Conference on Computer Vision (ICCV), Vol. 2, IEEE, 2005, pp. 1492–1499. [241] T. Yu, Y. Wu, Differential tracking based on spatial-appearance model (SAM), in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Vol. 1, IEEE, 2006, pp. 720–727. 269
  • 32. [242] R. Sicre, H. Nicolas, Improved Gaussian mixture model for the task of object tracking, in: Computer Analysis of Images and Patterns, Springer, 2011, pp. 389–396. [243] V. Karavasilis, C. Nikou, A. Likas, Visual tracking using the Earth Mover’s distance between Gaussian mixtures and Kalman filtering, Image and Vision Computing 29 (5) (2011) 295–305. [244] H. Wang, D. Suter, K. Schindler, C. Shen, Adaptive object tracking based on an effective appearance filter, IEEE Transactions on Pattern Analysis and Machine Intelligence 29 (9) (2007) 1661–1667. [245] A. Bhattacharyya, On a measure of divergence between two statistical pop- ulations defined by their probability distribution, Bulletin of the Calcutta Mathematical Society 35 (1) (1943) 99–109. [246] I. Leichter, M. Lindenbaum, E. Rivlin, Mean shift tracking with multiple ref- erence color histograms, Computer Vision and Image Understanding 114 (3) (2010) 400–408. [247] I. Leichter, M. Lindenbaum, E. Rivlin, Tracking by affine kernel transforma- tions using color and boundary cues, IEEE Transactions on Pattern Analysis and Machine Intelligence 31 (1) (2009) 164–171. [248] A. Babaeian, S. Rastegar, M. Bandarabadi, M. Rezaei, Mean shift-based object tracking with multiple features, in: IEEE 41st Southeastern Symposium on System Theory (SSST), IEEE, 2009, pp. 68–72. [249] H. Zhou, Y. Yuan, C. Shi, Object tracking using SIFT features and mean shift, Computer Vision and Image Understanding 113 (3) (2009) 345–352. 270
  • 33. [250] J. G. Allen, R. Y. Xu, J. S. Jin, Object tracking using Camshift algorithm and multiple quantized feature spaces, in: Proceedings of the Pan-Sydney Area Workshop on Visual Information Processing, Australian Computer Society, Inc., 2004, pp. 3–7. [251] J. Wang, Y. Yagi, Integrating color and shape-texture features for adap- tive real-time object tracking, IEEE Transactions on Image Processing 17 (2) (2008) 235–240. [252] H. Yin, Y. Chai, S. X. Yang, D. K. Chiu, An improved mean-shift tracking algorithm based on adaptive multiple feature fusion, in: Informatics in Control Automation and Robotics, Springer, 2011, pp. 49–62. [253] X. Zhang, Y. Yue, C. Sha, Object tracking approach based on mean shift algorithm, Journal of Multimedia 8 (3) (2013) 220–225. [254] R. T. Collins, Mean-shift blob tracking through scale space, in: IEEE Con- ference on Computer Vision and Pattern Recognition (CVPR), Vol. 2, IEEE, 2003, pp. 228–234. [255] C.-W. Juan, J.-S. Hu, A new spatial-color mean-shift object tracking algorithm with scale and orientation estimation, in: IEEE International Conference on Robotics and Automation (ICRA), IEEE, 2008, pp. 2265–2270. [256] J.-S. Hu, C.-W. Juan, J.-J. Wang, A spatial-color mean-shift object tracking algorithm with scale and orientation estimation, Pattern Recognition Letters 29 (16) (2008) 2165–2173. 271
  • 34. [257] X. Chen, Y. Zhou, X. Huang, C. Li, Adaptive bandwidth mean shift object tracking, in: IEEE Conference on Robotics, Automation and Mechatronics, IEEE, 2008, pp. 1011–1017. [258] J. Ning, L. Zhang, D. Zhang, C. Wu, Scale and orientation adaptive mean shift tracking, IET Computer Vision 6 (1) (2012) 52–61. [259] K. Quast, A. Kaup, Shape adaptive mean shift object tracking using Gaussian mixture models, in: Analysis, Retrieval and Delivery of Multimedia Content, Springer, 2013, pp. 107–122. [260] T. Vojir, J. Noskova, J. Matas, Robust scale-adaptive mean-shift for tracking, Pattern Recognition Letters 49 (2014) 250–258. [261] A. Yilmaz, Object tracking by asymmetric kernel mean shift with automatic scale and orientation selection, in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2007, pp. 1–6. [262] K. Quast, A. Kaup, Scale and shape adaptive mean shift object tracking in video sequences, in: 17th European Conference on Signal Processing (ECSP), IEEE, 2009, pp. 1513–1517. [263] A. Yilmaz, Kernel-based object tracking using asymmetric kernels with adap- tive scale and orientation selection, Machine Vision and Applications 22 (2) (2011) 255–268. [264] C. Shen, M. J. Brooks, D. H. A. Van, Fast global kernel density mode seek- ing: Applications to localization and tracking, IEEE Transactions on Image Processing 16 (5) (2007) 1457–1469. 272
  • 35. [265] G. Strang, Introduction to Linear Algebra, 4th Edition, Wellesley-Cambridge Press, Wellesley, MA, 2009. [266] C. M. Bishop, Pattern Recognition and Machine Learning, 1st Edition, Springer, Verlag, New York, 2006. [267] H. Lu, K. N. Plataniotis, A. N. Venetsanopoulos, A survey of multilinear subspace learning for tensor data, Pattern Recognition 44 (7) (2011) 1540– 1551. [268] M. J. Black, A. D. Jepson, Eigentracking: Robust matching and tracking of articulated objects using a view-based representation, International Journal of Computer Vision 26 (1) (1998) 63–84. [269] D. Skocaj, A. Leonardis, Weighted and robust incremental method for sub- space learning, in: IEEE International Conference on Computer Vision (ICCV), IEEE, 2003, pp. 1494–1501. [270] D. Wang, H. Lu, Y.-W. Chen, Incremental MPCA for color object tracking, in: IEEE International Conference on Pattern Recognition (ICPR), IEEE, 2010, pp. 1751–1754. [271] L. Wen, Z. Cai, Z. Lei, D. Yi, S. Z. Li, Online spatio-temporal structural context learning for visual tracking, in: European Conference on Computer Vision (ECCV), Springer, 2012, pp. 716–729. [272] T. Wang, I. Y. Gu, P. Shi, Object tracking using incremental 2D-PCA learning and ML estimation, in: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Vol. 1, IEEE, 2007, pp. 933–936. 273
  • 36. [273] D. Wang, H. Lu, Object tracking via 2D-PCA and regularization, IEEE Signal Processing Letters 19 (11) (2012) 711–714. [274] H. Lim, O. I. Camps, M. Sznaier, V. I. Morariu, Dynamic appearance modeling for human tracking, in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Vol. 1, IEEE, 2006, pp. 751–757. [275] T.-J. Chin, D. Suter, Incremental kernel principal component analysis, IEEE Transactions on Image Processing 16 (6) (2007) 1662–1674. [276] D. P. Huttenlocher, G. A. Klanderman, W. J. Rucklidge, Comparing images using the Hausdorff distance, IEEE Transactions on Pattern Analysis and Machine Intelligence 15 (9) (1993) 850–863. [277] R. T. Rockafellar, R. J.-B. Wets, Variational Analysis, 1st Edition, Springer Science & Business Media, Verlag, Berlin, Heidelberg, 2009. [278] G. Mori, J. Malik, Estimating human body configurations using shape context matching, in: European Conference on Computer Vision (ECCV), Springer, 2002, pp. 666–680. [279] J. Kang, I. Cohen, G. Medioni, Object reacquisition using invariant appearance model, in: IEEE International Conference on Pattern Recognition (ICPR), Vol. 4, IEEE, 2004, pp. 759–762. [280] Q. Xiaoping, Z. Qiheng, O. Yimin, M. Jiaguang, A method for object track- ing using shape matching, in: IEEE Workshop on Signal Processing Systems Design and Implementation, IEEE, 2006, pp. 372–376. [281] V. Ferrari, F. Jurie, C. Schmid, From images to shape models for object de- tection, International Journal of Computer Vision 87 (3) (2010) 284–303. 274
  • 37. [282] Z. Liu, H. Shen, G. Feng, D. Hu, Tracking objects using shape context match- ing, Neurocomputing 83 (2012) 47–55. [283] C. G. Zhao, T. G. Zhuang, A hybrid boundary detection algorithm based on Watershed and Snake, Pattern Recognition Letters 26 (9) (2005) 1256–1265. [284] Y. Xiang, A. C. Chung, J. Ye, An active contour model for image segmentation based on elastic interaction, Journal of Computational Physics 219 (1) (2006) 455–476. [285] Y. Rathi, N. Vaswani, A. Tannenbaum, A. Yezzi, Tracking deforming objects using particle filtering for geometric active contours, IEEE Transactions on Pattern Analysis and Machine Intelligence 29 (8) (2007) 1470–1475. [286] M. Kass, A. Witkin, D. Terzopoulos, Snakes: Active contour models, Interna- tional Journal of Computer Vision 1 (4) (1988) 321–331. [287] A. Yilmaz, X. Li, M. Shah, Contour-based object tracking with occlusion han- dling in video acquired using mobile cameras, IEEE Transactions on Pattern Analysis and Machine Intelligence 26 (11) (2004) 1531–1536. [288] M. S. Allili, D. Ziou, Object tracking in videos using adaptive mixture models and active contours, Neurocomputing 71 (10) (2008) 2001–2011. [289] L. D. Cohen, On active contour models and balloons, CVGIP: Image Under- standing 53 (2) (1991) 211–218. [290] C. Xu, J. L. Prince, Snakes, shapes, and gradient vector flow, IEEE Transac- tions on Image Processing 7 (3) (1998) 359–369. 275
  • 38. [291] S. Lefèvre, J.-P. Gérard, A. Piron, N. Vincent, An extended snake model for real-time multiple object tracking, in: RFAI: International Workshop on Advanced Concepts for Intelligent Vision Systems, Citeseer, 2002, pp. 268–275. [292] N. Ray, S. T. Acton, Motion gradient vector flow: An external force for track- ing rolling Leukocytes with shape and size constrained active contours, IEEE Transactions on Medical Imaging 23 (12) (2004) 1466–1478. [293] J.-H. Lee, F. Hua, J. W. Jang, An improved object detection and contour tracking algorithm based on local curvature, in: Signal Processing, Image Processing and Pattern Recognition, Springer, 2009, pp. 25–32. [294] J. Chiverton, X. Xie, M. Mirmehdi, Automatic bootstrapping and tracking of object contours, IEEE Transactions on Image Processing 21 (3) (2012) 1231– 1245. [295] J. Ning, L. Zhang, D. Zhang, W. Yu, Joint registration and active contour segmentation for object tracking, IEEE Transactions on Circuits and Systems for Video Technology 23 (9) (2013) 1589–1597. [296] V. Caselles, R. Kimmel, G. Sapiro, Geodesic active contours, International Journal of Computer Vision 22 (1) (1997) 61–79. [297] S. Osher, J. A. Sethian, Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations, Journal of Computational Physics 79 (1) (1988) 12–49. [298] N. Paragios, R. Deriche, Geodesic active contours and level sets for the detec- tion and tracking of moving objects, IEEE Transactions on Pattern Analysis and Machine Intelligence 22 (3) (2000) 266–280. 276
  • 39. [299] N. Paragios, O. Mellina-Gottardo, V. Ramesh, Gradient vector flow fast geodesic active contours, in: IEEE International Conference on Computer Vision (ICCV), Vol. 1, IEEE, 2001, pp. 67–73. [300] Y. Shi, W. C. Karl, Real-time tracking using level sets, in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Vol. 2, IEEE, 2005, pp. 34–41. [301] T. Brox, M. Rousson, R. Deriche, J. Weickert, Colour, texture, and motion in level set based segmentation and tracking, Image and Vision Computing 28 (3) (2010) 376–390. [302] R. F. Gonzalez, R. E. Woods, Digital Image Processing, 3rd Edition, Pearson Education, Singapore, 2008. [303] Y. Huang, Y. Huang, H. Niemann, Segmentation-based object tracking using image warping and Kalman filtering, in: IEEE International Conference on Image Processing (ICIP), Vol. 3, IEEE, 2002, pp. 601–604. [304] C. Kim, J.-N. Hwang, Fast and automatic video object segmentation and tracking for content-based applications, IEEE Transactions on Circuits and Systems for Video Technology 12 (2) (2002) 122–129. [305] A. Mittal, L. S. Davis, M2tracker: A multi-view approach to segmenting and tracking people in a cluttered scene, International Journal of Computer Vision 51 (3) (2003) 189–203. [306] T. Morimoto, O. Kiriyama, Y. Harada, H. Adachi, T. Koide, H. J. Mattausch, Object tracking in video pictures based on image segmentation and pattern 277
  • 40. matching, in: IEEE International Symposium on Circuits and Systems (IS- CAS), IEEE, 2005, pp. 3215–3218. [307] C. Wang, L. G. M. De, N. Paragios, Segmentation, ordering and multi-object tracking using graphical models., in: IEEE International Conference on Com- puter Vision (ICCV), IEEE, 2009, pp. 747–754. [308] V. Belagiannis, F. Schubert, N. Navab, S. Ilic, Segmentation based particle fil- tering for real-time 2D object tracking, in: European Conference on Computer Vision (ECCV), Springer, 2012, pp. 842–855. [309] X. Ren, J. Malik, Learning a classification model for segmentation, in: IEEE International Conference on Computer Vision (ICCV), IEEE, 2003, pp. 10–17. [310] A. Levinshtein, A. Stere, K. N. Kutulakos, D. J. Fleet, S. J. Dickinson, K. Sid- diqi, Turbopixels: Fast superpixels using geometric flows, IEEE Transactions on Pattern Analysis and Machine Intelligence 31 (12) (2009) 2290–2297. [311] M.-Y. Liu, O. Tuzel, S. Ramalingam, R. Chellappa, Entropy rate superpixel segmentation, in: IEEE Conference on Computer Vision and Pattern Recog- nition (CVPR), IEEE, 2011, pp. 2097–2104. [312] Z. Li, X.-M. Wu, S.-F. Chang, Segmentation using superpixels: A bipartite graph partitioning approach, in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2012, pp. 789–796. [313] Z. Li, J. Chen, Superpixel segmentation using linear spectral clustering, in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2015, pp. 1356–1363. 278
  • 41. [314] B. Liu, H. Hu, H. Wang, K. Wang, X. Liu, W. Yu, Superpixel-based classifi- cation with an adaptive number of classes for polarimetric SAR images, IEEE Transactions on Geoscience and Remote Sensing, 51 (2) (2013) 907–924. [315] R. Roscher, B. Waske, Superpixel-based classification of hyperspectral data using sparse representation and conditional random fields, in: IEEE Interna- tional Geoscience and Remote Sensing Symposium (IGARSS), IEEE, 2014, pp. 3674–3677. [316] S. Wang, H. Lu, F. Yang, M.-H. Yang, Superpixel tracking, in: IEEE Interna- tional Conference on Computer Vision (ICCV), IEEE, 2011, pp. 1323–1330. [317] W. Wang, R. Nevatia, Robust object tracking using constellation model with superpixel, in: Asian Conference on Computer Vision (ACCV), Springer, 2012, pp. 191–204. [318] X. Zhou, X. Li, T.-J. Chin, D. Suter, Superpixel-driven level set tracking, in: IEEE International Conference on Image Processing (ICIP), IEEE, 2012, pp. 409–412. [319] Z. Cai, L. Wen, Z. Lei, N. Vasconcelos, S. Z. Li, Robust deformable and occluded object tracking with dynamic graph, IEEE Transactions on Image Processing 23 (12) (2014) 5497–5509. [320] S. Theodoridis, K. Koutroumbas, Pattern Recognition, 4th Edition, Academic Press, USA, 2008. [321] R. E. Kalman, A new approach to linear filtering and prediction problems, Journal of Basic Engineering 82 (1) (1960) 35–45. 279
  • 42. [322] P. D. Moral, Non-linear filtering: Interacting particle resolution, Markov Pro- cesses and Related Fields 2 (4) (1996) 555–581. [323] J. S. Liu, R. Chen, Sequential Monte Carlo methods for dynamic systems, Journal of the American Statistical Association 93 (443) (1998) 1032–1044. [324] M. Isard, A. Blake, CONDENSATION - Conditional density propagation for visual tracking, International Journal of Computer Vision 29 (1) (1998) 5–28. [325] Z. Zhu, Q. Ji, K. Fujimura, K. Lee, Combining Kalman filtering and mean shift for real time eye tracking under active IR illumination, in: IEEE International Conference on Pattern Recognition (ICPR), Vol. 4, IEEE, 2002, pp. 318–321. [326] N. Funk, A study of the Kalman filter applied to visual tracking, University of Alberta, Project for CMPUT 652 (2003) 1–26. [327] E. V. Cuevas, D. Zaldivar, R. Rojas, Kalman filter for vision tracking, Tech- nical Report B 05-12, Freie University, Germany (2005). [328] S.-K. Weng, C.-M. Kuo, S.-K. Tu, Video object tracking using adaptive Kalman filter, Journal of Visual Communication and Image Representation 17 (6) (2006) 1190–1208. [329] D. Angelova, L. Mihaylova, Extended object tracking using mixture Kalman filtering, in: International Conference on Numerical Methods and Applica- tions, Springer, 2006, pp. 122–130. [330] Y. Yoon, A. Kosaka, A. C. Kak, A new Kalman-filter-based framework for fast and accurate visual tracking of rigid objects, IEEE Transactions on Robotics 24 (5) (2008) 1238–1251. 280
  • 43. [331] X. Li, K. Wang, W. Wang, Y. Li, A multiple object tracking method using Kalman filter, in: IEEE International Conference on Information and Automa- tion (ICIA), IEEE, 2010, pp. 1862–1866. [332] Z. Fu, Y. Han, Centroid weighted Kalman filter for visual object tracking, Measurement 45 (4) (2012) 650–655. [333] A. Salhi, A. Y. Jammoussi, Object tracking system using Camshift, meanshift and Kalman filter, World Academy of Science, Engineering and Technology 64 (2012) 674–679. [334] M. Isard, A. Blake, Condensation conditional density propagation for visual tracking, International Journal of Computer Vision 29 (1) (1998) 5–28. [335] Y. Wu, T. S. Huang, Robust visual tracking by integrating multiple cues based on co-inference learning, International Journal of Computer Vision 58 (1) (2004) 55–71. [336] C. Yang, R. Duraiswami, L. Davis, Fast multiple object tracking via a hierar- chical particle filter, in: IEEE International Conference on Computer Vision (ICCV), Vol. 1, IEEE, 2005, pp. 212–219. [337] Z. Khan, T. Balch, F. Dellaert, A Rao-Blackwellized particle filter for Eigen- tracking, in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Vol. 2, IEEE, 2004, pp. 974–980. [338] G. Casella, C. P. Robert, Rao-blackwellisation of sampling schemes, Biometrika 83 (1) (1996) 81–94. 281
  • 44. [339] S. K. Zhou, R. Chellappa, B. Moghaddam, Visual tracking and recognition using appearance-adaptive models in particle filters, IEEE Transactions on Image Processing 13 (11) (2004) 1491–1506. [340] P. Brasnett, L. Mihaylova, D. Bull, N. Canagarajah, Sequential Monte Carlo tracking by fusing multiple cues in video sequences, Image and Vision Com- puting 25 (8) (2007) 1217–1227. [341] M. Fotouhi, A. Gholami, S. Kasaei, Particle filter-based object tracking using adaptive histogram, in: IEEE 7th Iranian Conference on Machine Vision and Image Processing (MVIP), IEEE, 2011, pp. 1–5. [342] F. Gustafsson, F. Gunnarsson, N. Bergman, U. Forssell, J. Jansson, R. Karls- son, P.-J. Nordlund, Particle filters for positioning, navigation, and tracking, IEEE Transactions on Signal Processing 50 (2) (2002) 425–437. [343] C. Hue, J. L. Cadre, P. Pérez, Tracking multiple objects with particle filtering, IEEE Transactions on Aerospace and Electronic Systems 38 (3) (2002) 791– 812. [344] M. Jaward, L. Mihaylova, N. Canagarajah, D. Bull, Multiple object tracking using particle filters, in: IEEE Conference on Aerospace, IEEE, 2006, pp. 1–8. [345] X. Jia, H. Lu, M.-H. Yang, Visual tracking via adaptive structural local sparse appearance model, in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2012, pp. 1822–1829. [346] W. Zhong, H. Lu, M.-H. Yang, Robust object tracking via sparse collabora- tive appearance model, IEEE Transactions on Image Processing 23 (5) (2014) 2356–2368. 282
  • 45. [347] A. C. Copeland, M. M. Trivedi, Models and metrics for signature strength evaluation of camouflaged targets, in: AeroSense’97, International Society for Optics and Photonics, 1997, pp. 194–199. [348] F. M. Gretzmacher, G. S. Ruppert, S. Nyberg, Camouflage assessment con- sidering human perception data, in: Aerospace/Defense Sensing and Controls, International Society for Optics and Photonics, 1998, pp. 58–67. [349] J. Yu, Z. Cao, Q. Lai, The optimal camouflage pattern assessment and design in all conditions, Journal of Materials Science Research 4 (3) (2015) 76–97. [350] A. Toet, M. A. Hogervorst, Urban camouflage assessment through visual search and computational saliency, Optical Engineering 52 (4) (2013) 041103–041111. [351] S. K. Singh, C. A. Dhawale, S. Misra, Survey of object detection methods in camouflaged image, IERI Procedia 4 (2013) 351–357. [352] H. Du, X. Jin, X. Mao, Digital camouflage images using two-scale decomposi- tion, in: Computer Graphics Forum, Vol. 31, Wiley Online Library, 2012, pp. 2203–2212. [353] M. Harville, G. Gordon, J. Woodfill, Foreground segmentation using adaptive mixture models in color and depth, in: Proceedings on IEEE Workshop on Detection and Recognition of Events in Video, IEEE, 2001, pp. 3–11. [354] T. E. Boult, R. J. Micheals, X. Gao, M. Eckmann, Into the woods: Visual surveillance of noncooperative and camouflaged targets in complex outdoor settings, Proceedings of the IEEE 89 (10) (2001) 1382–1402. 283
  • 46. [355] P. KaewTrakulPong, R. Bowden, A real time adaptive visual surveillance sys- tem for tracking low-resolution colour targets in dynamically changing scenes, Image and Vision Computing 21 (10) (2003) 913–929. [356] Z. Q. Huang, Z. Jiang, Tracking camouflaged objects with weighted region consolidation, in: Proceedings on Digital Image Computing: Techniques and Applications (DICTA), IEEE, 2005, pp. 24–31. [357] T. Chandesa, T. Pridmore, A. Bargiela, Detecting occlusion and camouflage during visual tracking, in: IEEE International Conference on Signal and Image Processing Applications (ICSIPA), IEEE, 2009, pp. 468–473. [358] D. Conte, P. Foggia, G. Percannella, F. Tufano, M. Vento, An algorithm for detection of partially camouflaged people, in: 6th IEEE International Confer- ence on Advanced Video and Signal Based Surveillance (AVSS), IEEE, 2009, pp. 340–345. [359] A. Loza, L. Mihaylova, D. Bull, N. Canagarajah, Structural similarity-based object tracking in multimodality surveillance videos, Machine Vision and Ap- plications 20 (2) (2009) 71–83. [360] J. Y. Y. H. W. Hou, J. Li, Detection of the mobile object with camouflage color under dynamic background based on optical flow, Procedia Engineering 15 (2011) 2201–2205. [361] T. Malathi, K. M. Bhuyan, Foreground object detection under camouflage using multiple camera-based codebooks, in: Annual IEEE India Conference (INDICON), IEEE, 2013, pp. 1–6. 284
  • 47. [362] H. T. Nguyen, M. Worring, R. van den Boomgaard, A. Smeulders, Track- ing non-parameterized object contours in video, IEEE Transactions on Image Processing 11 (9) (2002) 1081–1091. [363] N. Paragios, O. Mellina-Gottardo, V. Ramesh, Gradient vector flow fast geo- metric active contours, IEEE Transactions on Pattern Analysis and Machine Intelligence 26 (3) (2004) 402–407. [364] F. Tang, S. Brennan, Q. Zhao, H. Tao, Co-tracking using semi-supervised Sup- port Vector Machines, in: IEEE 11th International Conference on Computer Vision (ICCV), IEEE, 2007, pp. 1–8. [365] A. Baumann, M. Boltz, J. Ebling, M. Koenig, H. S. Loos, M. Merkel, W. Niem, J. K. Warzelhan, J. Yu, A review and comparison of measures for automatic video surveillance systems, EURASIP Journal on Image and Video Processing 2008 (1) (2008) 1–30. [366] R. Kasturi, D. Goldgof, P. Soundararajan, V. Manohar, J. Garofolo, R. Bow- ers, M. Boonstra, V. Korzhova, J. Zhang, Framework for performance evalua- tion of face, text, and vehicle detection and tracking in video: Data, metrics and protocol, IEEE Transactions on Pattern Analysis and Machine Intelligence 31 (2) (2009) 319–336. [367] N. Lazarevic-McManus, J. R. Renno, D. Makris, G. A. Jones, An object- based comparative methodology for motion detection based on the F-measure, Computer Vision and Image Understanding 111 (1) (2008) 74–85. 285
  • 48. [368] W. Aitfares, E. Bouyakhf, A. Herbulot, F. Regragui, M. Devy, Hybrid region and interest points-based active contour for object tracking, Applied Mathe- matical Sciences 7 (118) (2013) 5879–5899. [369] W. T. Freeman, M. Roth, Orientation histograms for hand gesture recognition, in: IEEE Internatinal Workshop on Automatic Face and Gesture Recognition, IEEE, 1995, pp. 296–301. [370] K. Levi, Y. Weiss, Learning object detection from a small number of examples: the importance of good features, in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Vol. 2, IEEE, 2004, pp. 53–60. [371] F. Suard, A. Rakotomamonjy, A. Bensrhair, Pedestrian detection using In- frared images and histograms of oriented gradients, in: IEEE International Conference on Intelligent Vehicles (ICIV), IEEE, 2006, pp. 206–212. [372] J. M. Keller, M. R. Gray, J. A. Givens, A fuzzy K-nearest neighbor algorithm, IEEE Transactions on Systems, Man, and Cybernetics 15 (4) (1985) 580–585. [373] D. F. Specht, Probabilistic neural networks, Neural networks 3 (1) (1990) 109–118. [374] M. T. Musavi, K. H. Chan, D. M. Hummels, K. Kalantri, On the generalization ability of neural network classifiers, IEEE Transactions on Pattern Analysis and Machine Intelligence 16 (6) (1994) 659–663. [375] W. Yan, C. Weber, S. Wermter, A hybrid probabilistic neural model for person tracking based on a ceiling-mounted camera, Journal of Ambient Intelligence and Smart Environments 3 (3) (2011) 237–252. 286
  • 49. [376] W. Hao, B. Zhang, W. Tian, Head tracking by means of probabilistic neural networks, Measurement Science and Technology 18 (7) (2007) 1999–2009. [377] H. G. Traven, A neural network approach to statistical pattern classification by semiparametric estimation of probability density functions, IEEE Transactions on Neural Networks 2 (3) (1991) 366–377. [378] K. Z. Mao, K.-C. Tan, W. Ser, Probabilistic neural-network structure determi- nation for pattern classification, IEEE Transactions on Neural Networks 11 (4) (2000) 1009–1016. [379] C. M. Bishop, Neural Networks for Pattern Recognition, 1st Edition, Claren- don Press, Oxford, United Kingdom, 1995. [380] S. Krinidis, V. Chatzis, Fuzzy energy-based active contours, IEEE Transactons on Image Processing 18 (12) (2009) 2747–2755. [381] Y. Wu, W. Ma, M. Gong, H. Li, L. Jiao, Novel fuzzy active contour model with kernel metric for image segmentation, Applied Soft Computing 34 (2015) 301–311. [382] S. Challa, M. R. Morelande, D. Musicki, R. J. Evans, Fundamentals of Ob- ject Tracking, 1st Edition, Cambridge University Press, Cambridge, United Kingdom, 2011. [383] X. Lan, A. J. Ma, P. C. Yuen, Multi-cue visual tracking using robust feature- level fusion based on joint sparse representation, in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2014, pp. 1194– 1201. 287
  • 50. [384] T. Ojala, M. Pietikäinen, D. Harwood, A comparative study of texture mea- sures with classification based on featured distributions, Pattern Recognition 29 (1) (1996) 51–59. [385] M. A. Akhloufi, A. Bendada, Locally adaptive texture features for multispec- tral face recognition, in: IEEE International Conference on Systems, Man and Cybernetics (SMC), IEEE, 2010, pp. 3308–3314. [386] C. E. Metz, Basic principles of ROC analysis, in: Seminars in Nuclear Medicine, Vol. 8, Elsevier, 1978, pp. 283–298. [387] P. Burrascano, Learning vector quantization for the probabilistic neural net- work, IEEE transactions on Neural Networks 2 (4) (1990) 458–461. [388] P. Raghu, B. Yegnanarayana, Supervised texture classification using a proba- bilistic neural network and constraint satisfaction model, IEEE Transactions on Neural Networks 9 (3) (1998) 516–522. [389] A. Mondal, S. Ghosh, A. Ghosh, Efficient silhouette-based contour tracking using local information, Soft Computing 20 (2) (2016) 785–805. [390] Y. Pan, J. D. Birdwell, S. M. Djouadi, Efficient implementation of the Chan- Vese models without solving PDEs, in: IEEE Workshop on Multimedia Signal Processing, IEEE, 2006, pp. 350–354. [391] L. He, S. Osher, Solving the Chan-Vese model by a multiphase level set al- gorithm based on the topological derivative, in: International Conference on Scale Space and Variational Methods in Computer Vision, Springer, 2007, pp. 777–788. 288
  • 51. [392] C. Li, C.-Y. Kao, J. C. Gore, Z. Ding, Minimization of region-scalable fit- ting energy for image segmentation, IEEE Transactions on Image Processing 17 (10) (2008) 1940–1949. [393] S. Osher, J. A. Sethian, Fronts propagating with curvature dependent speed: Algorithms based on Hamilton-Jacobi formulation, Journal of Computational Physics 79 (1) (1988) 12–49. [394] C. W. Fox, An Introduction to the Calculus of Variations, 1st Edition, Oxford University Press, New York, United Kingdom, 1988. [395] B. Song, T. Chan, A fast algorithm for level set based optimization, UCLA Cam Report 68 (2002) 2–68. [396] L. Sevilla-Lara, E. Learned-Miller, Distribution fields for tracking, in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2012, pp. 1910–1917. [397] J. F. Henriques, R. Caseiro, P. Martins, J. Batista, High-speed tracking with kernelized correlation filters, IEEE Transactions on Pattern Analysis and Ma- chine Intelligence 37 (3) (2015) 583–596. [398] Y. Wu, J. Lim, M.-H. Yang, Online object tracking: A benchmark, in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2013, pp. 2411–2418. [399] C. Desai, D. Ramanan, C. C. Fowlkes, Discriminative models for multi-class object layout, International Journal of Computer Vision 95 (1) (2011) 1–12. [400] M. Yang, Y. Wu, G. Hua, Context-aware visual tracking, IEEE Transactions on Pattern Analysis and Machine Intelligence 31 (7) (2009) 1195–1209. 289
  • 52. [401] J. Kwon, K. M. Lee, Visual tracking decomposition, in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2010, pp. 1269– 1276. [402] J. Kwon, K. M. Lee, Tracking by sampling trackers, in: IEEE International Conference on Computer Vision (ICCV), IEEE, 2011, pp. 1195–1202. [403] S. He, Q. Yang, R. Lau, J. Wang, M.-H. Yang, Visual tracking via locality sensitive histograms, in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2013, pp. 2427–2434. [404] C. Leistner, M. Godec, A. Saffari, H. Bischof, On-line multi-view forests for tracking, in: IEEE International Conference on Pattern Recognition (ICPR), 2010, pp. 493–502. [405] N. Jiang, W. Liu, Y. Wu, Adaptive and discriminative metric differential tracking, in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2011, pp. 1161–1168. [406] Z. Kalal, K. Mikolajczyk, J. Matas, Tracking-learning-detection, IEEE Trans- actions on Pattern Analysis and Machine Intelligence 34 (7) (2012) 1409–1422. [407] M. Danelljan, G. Häger, F. Khan, M. Felsberg, Accurate scale estimation for robust visual tracking, in: British Machine Vision Conference (BMVA), BMVA Press, 2014, pp. 1–11. [408] A. W. Smeulders, D. M. Chu, R. Cucchiara, S. Calderara, A. Dehghan, M. Shah, Visual tracking: An experimental survey, IEEE Transactions on Pattern Analysis and Machine Intelligence 36 (7) (2014) 1442–1468. 290
  • 53. [409] L. I. Kuncheva, Combining Pattern Classifiers: Methods and Algorithms, 1st Edition, John Wiley & Sons, Hoboken, New Jersey, 2004. [410] S. Haykin, Neural Networks A Comprehensive Foundation, 1st Edition, Pren- tice Hall, Inc., New Jersey, U.S.A, 1999. [411] H. Kurokawa, C.-Y. Ho, S. Mori, A novel back propagation algorithm with optimal number of hidden units, in: International Conference on Artifical Neural Network, Springer, 1993, pp. 783–783. [412] R. Benmokhtar, B. Huet, Neural network combining classifier based on Dempster-Shafer theory for semantic indexing in video content, in: Advances in Multimedia Modeling, Springer, 2007, pp. 196–205. [413] D.-S. Lee, S. N. Srihari, A theory of classifier combination: the neural net- work approach, in: 3rd International Conference on Document Analysis and Recognition (DAR), Vol. 1, IEEE, 1995, pp. 42–45. [414] S. X. Liao, M. Pawlak, On image analysis by moments, IEEE Transactions on Pattern Analysis and Machine Intelligence 18 (3) (1996) 254–266. [415] R. Mukundan, K. R. Ramakrishnan, Moment Functions in Image Analysis: Theory and Applications, 1st Edition, World Scientific, Singapore, 1998. [416] J. Zhang, S. Ma, S. Sclaroff, MEEM: robust tracking via multiple experts using entropy minimization, in: European Conference on Computer Vision (ECCV), Springer, 2014, pp. 188–203. [417] L. Wang, W. Ouyang, X. Wang, H. Lu, Visual tracking with fully convolutional networks, in: Proceedings of the IEEE International Conference on Computer Vision (ICCV), IEEE, 2015, pp. 3119–3127. 291
  • 54. [418] O. Mazhelis, One-class classifiers: A review and analysis of suitability in the context of mobile-masquerader detection, South African Computer Journal 36 (36) (2006) 29–48. [419] S. S. Khan, M. G. Madden, A survey of recent trends in one class classification, in: Artificial Intelligence and Cognitive Science, Springer, 2009, pp. 188–197. [420] S. S. Khan, M. G. Madden, One-class classification: Taxonomy of study and review of techniques, The Knowledge Engineering Review 29 (3) (2014) 345– 374. 292