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2D/Multi-view Segmentation and Tracking

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Touradj Ebrahimi
Touradj Ebrahimi

My seminar at VISNET-II Summer School, June 15-19, 2009

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2D/Multi-view Segmentation and Tracking Prof. Dr. Touradj Ebrahimi Multimedia Signal Processing Group Ecole Polytechnique Fédérale de Lausanne (EPFL)
Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Introduction ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Applications ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Segmentation ,[object Object],[object Object],[object Object],[object Object],[object Object]
Regions and objects ,[object Object],application dependent Semantically meaningful: selection depends on the application Objects Homogeneous according to given criteria (color, motion, texture...): automatically extracted and tracked. Regions
Mathematical formulation ,[object Object],[object Object]
Segmentation techniques ,[object Object],[object Object],[object Object],[object Object]
Histogram shape analysis ,[object Object],[object Object],[object Object],T Observed Background Object
Edge-based segmentation techniques ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Region-based segmentation ,[object Object],[object Object],[object Object],[object Object],[object Object]
Region-based segmentation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Splitting and merging ,[object Object],[object Object],[object Object],[object Object]
The multiple feature approach ,[object Object],[object Object],[object Object],[object Object]
The multiple feature approach ,[object Object],[object Object],[object Object],Texture Motion (v x , v y ) Color (Y,U,V,R,G,B…) Position (x,y) image
The multiple feature approach ,[object Object],motion color texture R 1 R 2 R 3  1  2  3
Fuzzy C-Means ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Fuzzy exponent
Fuzzy C-Means Stability? Initialize membership matrix U Update centroids: minimize objective function J(U,  with constant U Update memberships: minimize objective function J(U,  with constant  begin end Algorithm: yes no
Tracking ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Multilevel Region-object Tracking Procedure ,[object Object],[object Object],[object Object],[object Object],[object Object]
2D Tracking ,[object Object]
Example of 2D segmentation and tracking ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
2D segmentation and tracking
Typical results of Multilevel Region-object tracking
Typical results of Multilevel Region-object tracking
Multi-view Tracking ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Overview of a multi-view tracking system developed in VISNET-II
Consistent Object Labeling ,[object Object]
Object Consistency Verification ,[object Object],O i (n+1) O i (n) R i,j (n) R i,j (n+1)
Objects Correspondence ,[object Object],[object Object],[object Object],[object Object]
Objects Correspondence View 1 View 2 Homography transform of View 1 to View 2
Transfer Error ,[object Object],[object Object],[object Object]
Correspondence Verification ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Results
Results
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Motivation for Unusual Events Detection
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Motivation for Unusual Events Detection ,[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],State-of-the-art
[object Object],System Overview
[object Object],[object Object],[object Object],[object Object],[object Object],Technical Approach
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Technical Approach (cont.)
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Technical Approach (cont.)
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Technical Approach (cont.)
[object Object],[object Object],[object Object],[object Object],[object Object],Experiments and Results ,[object Object],[object Object],[object Object],[object Object]
[object Object],Experiments and Results (cont.)
[object Object],Experiments and Results (cont.) Testing sequence Video duration (Number of frames) Number of unusual trajectories (Number of trajectories) Average unusual trajectory length in number of frames (Average trajectory length) Training sequence Unusual events detection rate False alarms S1 4 min 25 sec (6642) 5 (26) 135 (477) S1 (2-fold cross-validation) 2/2 none S2 3 min 49 sec (5752) 2 (24) 218 (450) S1 2/2 none S3 1 min 50 sec (2551) 8 (41) 112 (222) S1 8/8 2 S4 1 min 42 sec (2556) 8 (49) 80 (164) S2 7/8 4
[object Object],[object Object],[object Object],[object Object],[object Object],Challenges
[object Object],[object Object],Conclusions
Thanks for your attention ! Questions, discussions, … Acknowledgements goes to my past and present PhD students who have contributed and continue to contribute to this work: Andrea Cavallaro, Olivier Steiger, Emrullah Durucan, Yousri Abdeljaoued, Ivan Ivanov, as well as Gelareh Mohammadi (research assistant in 2008)

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2D/Multi-view Segmentation and Tracking

  • 1. 2D/Multi-view Segmentation and Tracking Prof. Dr. Touradj Ebrahimi Multimedia Signal Processing Group Ecole Polytechnique Fédérale de Lausanne (EPFL)
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  • 18. Fuzzy C-Means Stability? Initialize membership matrix U Update centroids: minimize objective function J(U,  with constant U Update memberships: minimize objective function J(U,  with constant  begin end Algorithm: yes no
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  • 24. Typical results of Multilevel Region-object tracking
  • 25. Typical results of Multilevel Region-object tracking
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  • 27. Overview of a multi-view tracking system developed in VISNET-II
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  • 31. Objects Correspondence View 1 View 2 Homography transform of View 1 to View 2
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  • 49. Thanks for your attention ! Questions, discussions, … Acknowledgements goes to my past and present PhD students who have contributed and continue to contribute to this work: Andrea Cavallaro, Olivier Steiger, Emrullah Durucan, Yousri Abdeljaoued, Ivan Ivanov, as well as Gelareh Mohammadi (research assistant in 2008)