50120140502009

International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),
INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING &
ISSN 0976 - 6375(Online), Volume 5, Issue 2, February (2014), pp. 71-87 © IAEME

TECHNOLOGY (IJCET)

ISSN 0976 – 6367(Print)
ISSN 0976 – 6375(Online)
Volume 5, Issue 2, February (2014), pp. 71-87
© IAEME: www.iaeme.com/ijcet.asp
Journal Impact Factor (2014): 4.4012 (Calculated by GISI)
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IJCET
©IAEME

CROWD BEHAVIOUR ANALYSIS CONSIDERING INTER-PERSONNEL
ACTIVITIES IN SURVEILLANCE SYSTEMS
Najmuzzama Zerdi1,

Dr. Subhash S Kulkarni2,

Dr. V. D. Mytri3,

Kashyap D Dhruve4

1

2

(Professor and Head, Dept. of E&C, K.C.T.E.C Gulbarga- 585101, Karnataka, India)
(Professor and Head, Dept. of E&C, PES Institute of Technology - Bangalore South Campus,
Bangalore India)
3
(Principal, AIET, Gulbarga, Karnataka, India)
4
(Technical Director, Planet-I Technologies, Bangalore, India)

ABSTRACT
In public and private places, people panic when unpredicted events occur. The modelling and
analysis of the crowd surveillance videos is an issue that exists due to the unconstrained behaviors
observed. The use of computer aided techniques to address this issue has been proposed by
researchers. Similarly, we proposed a Graph theoretic approach based Crowd Behavior Analysis and
Classification System (GCBACS) model in this paper. The crowd personnel behavior is observed and
graph theoretic approach is used for analysis and classification of the activity observed. Streak Flows
is considered to observe crowd behaviors. The novelty of GCBACS is that considers inter personnel
activities for analysis and classification. The GCBACS is evaluated using varied surveillance videos
in the experimental study. The results presented prove that the GCBACS is able to achieve accurate
classification results in dense and sparse crowd scenarios. The GCBACS is compared with the
Spatio-Temporal Viscous Fluid Field method. The results prove that the GCBACS outperforms the
Spatio-Temporal Viscous Fluid Field method.
Keywords: Video Surveillance, Crowd Behavior, Crowd Motion, Optical Flow, Streak Lines, Streak
Line Flow, Path Lines, Threshold, Graph Theory.
1. INTRODUCTION
Now days video surveillance systems are fed by multiple high definition video streams, they
have become a very common feature in public and private spaces. The video surveillance systems
can be implemented manually and the large volume of data captured from high-throughput
71


surveillance system make manual analysis extremely time consuming, prone to human error. The
steady population growth observed in the past decade have resulted large crowd movements
especially in public spaces like airports , train stations , bus stations, shopping malls, religious places,
etc. The number of crowd accidents observed have increased in the recent times [1]. The automated
system, which is used to classify the movements of crowds or detect abnormal activity, can be
considered as an open research issue. A crowd can be considered as a collection of people distributed
over the region of interest and tracking of human activity or personnel counting within video
surveillance systems has been researched upon [3-4] for some time now. The open research issues
that exist and require attention with respect to crowd analysis can be listed as modeling or knowledge
extraction from crowd patterns [5-8] and crowd behavior analysis [9-11]. Limited work is carried
out to classify the behavior of crowds in surveillance systems. The research work presented in this
paper introduces the Graph theoretic approach based Crowd Behavior Analysis and Classification
System (
). To achieve accurate classification results the behavior of the personnel in the
crowd needs to be analyzed first. The behavior of the personnel in the crowd can be analyzed based
on the motion or trajectory activities observed. Based on the behavior of the personnel analyzed, it
can be classified into normal or abnormal activity. Abnormal activity detection is achieved by
observing unusual behavior of personnel or group of personnel within a crowd. Abnormality
detection refers to the detection of unusual behavior of individuals or a group in a crowd scene and
the Activities like instantaneous disbursement, sudden convergence or fighting are classified as
abnormal activities.
To analyze the behavior of personnel in the crowd, tracking methodologies are generally
used. The commonly used tracking methodologies [2] [3] [4] implemented and it is fail when large
crowds are considered. To overcome this drawback, researchers proposed the consideration of fixed
cell sizes to identify local trajectories and later map it together to detect the personnel trajectory
patterns [8] [10] [12]. However, the frames are split into uniform cells in these approaches. The use
of optical flow techniques within each cell is considered to obtain the trajectory patterns of personnel
within a cell. For instance, uses the optical flow techniques exhibited better results when compared
to traditional tracking methodologies [13]. The drawback of the optical flow is that only two
consecutive frames are considered to obtain personnel trajectory patterns. The optical flow method
are not able to capture long term does temporal dependencies [14]. To overcome this drawback the
concept of particle flow was introduced in [7]. The particle flow computation is achieved by
displacing a grid of particles with optical flow through numerical integration techniques, which
providing trajectories that relate a particles original position to its position at a later time. The
particle flow mechanisms proved to be computationally very heavy and minute personnel motion
details were ignored. The introduction of streak lines flows to obtain the trajectories of personnel in
the crowd proved to provide accurate analysis results [13]. For crowd behavior classification in [15]
an unsupervised machine learning technique based framework was proposed. The framework in [16]
considered hierarchical Bayesian models to connect the visual features, “atomic” activities and the
interactions for classification. In [12] streak-lines coupled with social force models were used to
detect abnormal activities.
The work carried out so far by researchers, primarily concentrates on analysis of activities
amongst a few personnel present in the crowd only, and do not take into account the inter personnel
activities for classification. To overcome this drawback the
presented in this paper which
considers inter personnel activities for analysis. The inter personnel activity consideration, enables
analysis in dense and sparse crowd scenarios. The inter personnel activities are monitored through
the motion vectors observed. To acquire the behavioral vectors of personnel in the crowd video an
optical flow is initially computed. Based on the optical flow the path lines and streak lines are
presented here. However, the path lines, streak lines are used to derive the streak flow vectors which
72


define the potential and personnel flow in crowds. Every frame of the video is analyzed using graph
theoretic approaches and it is also considers simultaneously density, direction, velocity, focuses
analysis on specific regions where the density of the motions is high. The GCBACS considers each
frame as a graph with sub graphs. All the frames are analyzed and the cumulative variance is
computed. If the GCBACS observes that the cumulative variance is greater than a threshold the
activity of the personnel in the crowd is classified as an abnormal activity.
The remaining manuscript is organized as follows. Section two discusses the related work.
The GCBACS is discussed in section three of the paper. The experimental study conducted to
evaluate the performance of the GCBACS is discussed in section four of the paper. The conclusion is
discussed in last section in this paper.
2. LITERATURE SURVEY
In this section of the paper a brief of the literature review conducted during the course of the
research work presented here is discussed. Most related works focus on the analysis of activities
introduced by some individuals between them.
Myo Thida et al. [6] proposed a manifold learning-based framework for the detection of
anomalies that occur in a crowded scene. The spatiotemporal LE method is employed to study the
local motion structure of the scene. The pair-wise graph is constructed by considering the visual
context of multiple local patches in both spatial and temporal domains. The drawbacks of this paper
related to localization of the abnormal regions.
Kristjan Greenewald et al [7] proposed to learn the normative multi-frame pixel joint
distribution and detect deviations from it using a likelihood based approach. Due to the extreme lack
of available training samples relative to the dimension of the distribution, a mean and covariance
approach and consider methods of learning the spatio-temporal covariance in the low-sample regime.
The space-time pixel covariance for crowd videos can be effectively represented as a sum of
Kronecker products using only a few factors, when adjustment is made for steady flow if present.
Yong Wang et al. [8] represents an abnormal behavior detection approach which is
convenient and available for camera sensor networks. Trajectory analysis and anomaly modeling are
carried out by single-node processing; therefore, the anomaly detection is performed by multimode
voting. A first-order Markov model is used to build the anomaly transition matrix and the detection
threshold can be determined by training the obtained symbol sequences. The voting mechanism
reaches a final decision in accordance with local decisions of camera nodes. The drawbacks of this
method that it is undesirable for complex abnormal behavior detection to only consider the trajectory
information.
Xiaobin Zhu et al.[9] proposed a weighted interaction force estimation in the social force
model(SFM)-based framework, in which the properties of surrounding individuals in terms of
motion consistence, distance apart, and angle-of-view along moving directions are fully utilized in
order to more precisely discriminate normal or abnormal behaviors of crowd. To avoid the
challenges in object tracking in crowded videos, here they perform particle advection to capture the
continuity of crowd and use these moving particles as individuals for the interaction force estimation.
the properties of surrounding individuals in terms of motion consistence, distance apart, and angleof-view along moving directions are fully explored, in order to more precisely discriminate normal
or abnormal behaviors of crowd.

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3. GRAPH THEORETIC APPROACH BASED CROWD BEHAVIOR ANALYSIS AND
CLASSIFICATION SYSTEM (GCBACS)
3.1 SYSTEM PRELIMINARIES

model overview

Figure 1:

We present a surveillance video
.Let us consider, the video represents a set of frames
and the dimension of each image is
pixels. A frame at the time instance and
.
Similarly the frame at the
time instance is represented as .The frame
is split into a
number of blocks and a mesh based structure is created for computational ease. Let the set
represent the crowd personnel to be observed in the surveillance video space . The set consists
of
personnel. The trajectory of the
personnel i.e.
at the time instance can be
represented as
. At the initial instance i.e.
the trajectory is represented
as
. Generally, the trajectories is used for optical flow computations. From the optical
flows we can observed that the streak lines and path lines of the personnel in the crowd are
is derived which is used for analysis. For
computed frame wise. Therefore, the streak flow
analysis a graph theoretic approach is adopted in the graph theoretic approach based Crowd Behavior
Analysis and Classification System GCBACS. We denote each frame as a graph and analysis is
carried out based on the similarity and deviations are noticed. In order to effectively recognize the
Cumulative variance
is computed considering all the previous frames and the current frame.
Therefore, if the variance is greater than the threshold
then abnormal activity is said to be
modeling is illustrated in Figure 1.
detected. The proposed
3.2 OPTICAL FLOW ESTABLISHMENT
In GCBACS the Lucas & Kanade based methodology is used to compute the differential
optical flow of the crowd vectors. The optical flow enables trajectory detection of personnel in the
crowd. Let the velocity field defined over the set be represented as
. The velocity satisfy the
continuity in time
and continuity in space
domain to obtain smooth optical flows. To achieve
optical flow computation a hierarchical graph structure is considered to represent the video
.
74


Let the levels of the graph be defined as
. If represents the velocity then the
optical flow residual vector
is used to minimizes the function vector
. Similarly the
matching function can be minimized using the residual vector . The primary guess for the
level of the optical flow is denoted as
. The value of is obtained by optical flow
computations from
to
. The frame and can be represented on the basis of the optical
flows computed at all the levels and is represented as

From the above equation, we can denoted as,
are two integer values and and
represent the previous two frames. Based on the above equations it can be acquired that there exist a
and
. The optical flow representation of the
domain definition difference between
which is defined over the window size
instead of
frame
using
. Let us Consider that the displacement vector is
and image position vector is
.the matching function
is minimizes by the vector
.The function
is represents as

An iterative Lucas –Kanade method is adopted to solve the function

From the first order expansion about the point
can described as

Therefore, the
point is described as

and is represented as

instead of

is the temporal frame image derivative at the point

75

Equation 4

. The


The derivative at

is denoted as

From Equation 5we can denote ,

from

The derivatives
in the
the next image

Based on

and

is the gradient vector and

can be defined as

and

can be evaluate directly from the image
, which is a neighborhood of the point
independently
. The derivative images satisfy the expression
and can be represents as

defined above the computation of

Equation 12 can be further simplified and written as:

Where

76

is given as


As

and matrix

is invertible, then the optical flow vector

Based on the above equation we can observed that

is defined as

, it is evident that

contains

information of the gradients in the and direction of . A large number of iterations have to be
represent
considered to obtain accurate optical flow of personnel in the crowd video frames. Let
the number of iterations required and
. Based on the optical flow computations from
the initial guess
for pixel displacement is obtained. The initial guess is
given as
. If represents the new image based on
, provided
then

Therefore, using optical flow methodology in
and mismatch vectors are obtained. The residual vector
function

, defined as

Using Equation 14 the solution of

Where the mismatch vector matrix

In Equation 16

the residual pixel trajectory vector
minimizes the error

represents the

can be obtained and is defined as

is given as

frame difference and is defined as

77


From Equation 18, we can observe that the spatial derivatives
and are computed only
once initially and is constant for an entire iteration loop. The parameter vector
is iteratively
steps. That vector
is the amount of residual difference between the video frames
computed at
after translation by the vector
. Based on the matrix and ,
is computed. The new pixel
displacement is
that is computed in the step
and is defined as

is below a preset
The iteration steps to achieve convergence are repeated till the value of
threshold or the
number of maximum iterations are completed. If represents the number of
iterations required to reach convergence then the optical flow vector can be defined as

The optical flow based on the velocities in the and
be represented as

direction at the

time instance can

3.3 STREAK-LINE FLOW ESTABLISHMENT
First, we introduced a streak-line representation of flow based on the optical flow and optical
flow computed present gaps in the trajectories of personnel in the crowd. In GCBACS the gaps in the
optical flow of similar motion vectors are filled using the streak lines [15] and new trajectory vectors
are established prior to analysis using graph theoretic approaches.
Let us consider a particle at position in the tth time instance, present in the frame and it is
represented as
. The advection of the particle is achieved by

are obtained from the optical flow vectors. For all the frames and time
using particle advection we can acquire a vector matrix. The columns of the matrix can
be used to obtain the particle trajectory details from time to the current time that is described as
path lines. The
is introduced to represent the path lines. The row of the matrix can be used
to define the streak lines that connect the particles from video frames that generated from the
position . In this paper, the streak lines is represented using
notation Inconstancies are
identified in the streak lines. The drawback in [15] the extended particle was introduced based on the
position
and the optical flow velocities can be overcome by proposed method. The th extended
particle can be denoted as
78


Where,

and

.

As per the streak lines the behavior of the personnel is obtained. Using the streak lines, the streak
flow is computed and is defined as

Therefore, the streak line computation is realized by integrating the optical flows computed
have to be computed. The computation of
and forming extended particles. To compute , and
and
are similar in nature. If
then let us consider a vector
to obtain the
has three pixels as its neighbors which forms
streak flow in the direction. The extended particle
a triangle. is considered as the interpolations of the neighboring pixels and is represented as

From the Equation 26

identify the index of the pixel,
and

Using the interpolation method the parameters
vectors in
and based on Equation 26 we can state

Then,

are the elements of the matrix

represents the basis function.
are obtained. For all the

. Therefore, following equation, 25 and 26

similarly
can be computed. Using
and
the streak flow
is acquired. In Graph theoretic
model use of streak
approach based Crowd Behavior Analysis and Classification System
flow to acquire the trajectory of the personnel in the crowd is considered as the streak flow
methodology enables instantaneous change observation when compared to particle flows.
3.4 GRAPH THEORETIC APPROACH FOR ANALYSIS AND CLASSIFICATION
In GCBACS model the behavior of crowd personnel noticed using the streak flow is analyzed
using a graph structure, which is adopted for all the frames of the video. Let us consider is a
constructed graph
obtained from the streak flow . The vertices of the graph are the
number of pixel vectors noticed and represents as

Therefore, the edges of the graph

can be represented as

Where, streak flow computed represents a planar field, and
based on the
decomposition obtained by Helmholtz. is the irrotational part of the vector field and
is the
79


incompressible part. In [22] two functions are introduced such that,
.However,
the stream function and the velocity potential function are described using Fourier Transforms
introduced in [22]. The functions and are represent as

Then it follows immediately that the function contribute details of the steady motion
vectors and contributes details of the random motion changes detected. Therefore, By combining
the and vectors the potential functions of the video frame is computed and the edge set can be
represented as

Therefore, an abnormal event can be detected from the consecutive frames is considered i.e.
graph
and graph
the relation between the graphs can also be examined as the relation
amongst the sub sets of and
and is represented as

represents the number of vectors
Based on the above equation we can denoted that
common to the graphs
and
. The number of nonaligned graph nodes in the two
frames is represented by

Therefore,
is the number of matched sub graphs identify in the graph frames
and
.
represents the sub sets of .
represents the sub sets of
The matching of the energy potential functions of the frames is represents as

80


However, the local differences in the sub sets are measured to observed the finer movements
in the graphs and is computed using

is a function that defines the matching between the sub
During the process
sets
and
. Combining all the above definitions the variance between two frames represented
as graphs and calculated as

Where
be given as

is a predefined integer and the cumulative variance noticed till the

frame can

Therefore, if the value of the cumulative variance is greater than a predefined threshold
then abnormal event is identified in the video and it is assigned the class 1 else 0. The classification
can be described as

Having discussed the modelling of crowd behavior using streak flows and analysis using
graph theoretic approaches in the
, the next section of this paper presents an experimental
study considering the proposed system.
4. EXPERIMENTAL STUDY AND PERFORMANCE EVALUATION
The effectiveness of the performance of GCBACS model is evaluated on the data set from
University of Minnesota [23] and a web data set. The video sequences [23] consists of abnormal and
normal crowd personnel videos and is referred to as Case 1. The web data set consists of a video
sequence of two persons and is referred to as Case 2 hereafter. In Case 2 a person pushes the other
person from behind and then runs. The purpose of the GCBACS model is to detect the abnormal
behavior in Case 1, i.e. sudden dispersal of crowd personnel and the falling of the person in Case 2.
The performance of the GCBACS is compared with the Spatio-Temporal Viscous Fluid Field (
)
81


method proposed in [17]. The Matlab platform is used to develop GCBACS model. To represent the
recognition results the authors of this paper have considered the use or bar charts. The green section
represent normal crowd activity and the red section represent abnormal crowd activity. The use of
receiver operating characteristic curves (ROC) [24] has been considered to evaluate the classification
performance. The area under the ROC curve and classification error plots are considered to evaluate
the performance of GCBACS and Spatio-Temporal Viscous Fluid Field (VFF) method.
4.1 CASE 1 :STUDY USING UNIVERSITY OF MINNESOTA DATASET

Figure 2: Comparison results of crowd behavior recognition and classification for videos in
Case 1 Using
and
. The ground Truth is also shown
The recognition results obtained, considering GCBACS and VFF for Case 1 is shown in
Figure 3. From the figure it is clear that the GCBACS achieves better activity classification when
is 0.18 and for GCBACS is 0.5.
compared to the VFF method. The misclassification ratio for
The unconstrained movement of people in the crowd observed by the Spatio-Temporal Viscous Fluid
Field (VFF) method resulted in a larger number of misclassified frames seen by the small red
intermediate regions. The misclassification ratio of
is lower as inter personnel activities are
considered for classification. The ROC curve obtained is shown Figure 3. The
curves prove that
the GCBACS accurately classifies and analyzes the crowd behavior in Case 1 when compared to
the
scheme. The area under the ROC curve considering GCBACS was found to be 0.88 and for
0.67 and is shown in Figure 4. The average classification error considering GCBACS was found to
be 0.17. In the case of the Spatio-Temporal Viscous Fluid Field (VFF) the average classification
error is 0.29. The results obtained for the classification errors observed is shown in Figure 5.

82


Figure 3:

Curves for Crowd Activity Classification based on

Figure 4: Area under

Curve considering

Figure 5: Classification Error considering
83

and

and

and

for Case 1

for Case 1

for Case 1


4.2 CASE 2 : STUDY USING WEB DATASET
In Case 2 the surveillance video consists of a sparse crowd scenario i.e. only two people. In
such conditions the interpersonal behavior capture is critical to achieve accurate analysis. The
recognition results obtained considering GCBACS and VFF are shown in Figure 6. From the figure
it is clear that the proposed GCBACS exhibits higher efficiency to capture interpersonal behavior
when compared to the Spatio-Temporal Viscous Fluid Field (VFF) method. This is also proved by
the fact that a lower number of false positives are seen in the bars. The misclassification ratio is 0.22
and 0.03 for GCBACS and VFF. The classification accuracy of the GCBACS when compared to the
VFF is proved by the ROC curve plot shown in Figure 7. The area under the ROC curve is around
0.98 for GCBACS and 0.73 for VFF. The area under the ROC curve graph is shown in Figure 8. The
classification error observed is shown in Figure 9 of this paper. The average classification error
for
is 0.24 and for
is 0.09.

Figure 6: Comparison results of crowd behavior recognition and classification for videos in
Case 2 Using
and
. The ground Truth is also shown

Figure 7:

Curves for Crowd Activity Classification based on
84

and

for Case 2


AREA UNDER ROC CURVE
1.2
1

AREA

0.8
0.6
GCBACS

0.4

VFF

0.2
0
0

ALGORITHM NAME

Figure 8: Area under

Curve considering

and

for Case 2

CLASSIFICATION ERROR
0.4

CLASSIFICATION ERROR

GCBACS

VFF

0.2

0
0

1

2

3

Figure 9: Classification Error considering

4

and

5

for Case 2

The results presented this paper prove that the proposed GCBACS is efficient and capable of
analysis of crowd behaviors in the case of sparse (i.e. Case 2) and dense (i.e. Case 1) crowd
surveillance videos. The GCBACS exhibits better performance when compared to the state of art
Spatio-Temporal Viscous Fluid Field (VFF) method.
5. CONCLUSION AND FUTURE WORK
Monitoring surveillance videos is currently achieved manually due to the random behavior of
people in the crowd. Modelling and classification of crowd activities is a problem that exists. To
address this issue, a computer aided Crowd Behavior Analysis and Classification System (GCBACS)
is discussed in this paper. The current research systems in place do not consider inter personal
behavior as an important factor for crowd behavior analysis. The GCBACS emphasis on the
consideration of inter personal behavior for analysis. The behavioral features of the crowd are
85


derived using the streak flows. The streak flows observed in each frame of the surveillance videos
are considered as graphs. The cumulative variance is computed from the graphs and a threshold
based scheme is used in
to classify the crowd activity into normal and abnormal behaviors.
The results presented in this paper discuss the performance evaluation between
and the
Spatio-Temporal Viscous Fluid Field method for sparse and dense crowd cases. The performance
improvement of the GCBACS is proved in terms of recognition results, ROC curves, area under the
ROC curve and classification parameters.
ACKNOWLEDGEMENTS
The authors of this paper would like to thank Mr Ashutosh Kumar and Mr Kashyap Dhruve
for their valuable suggestion and support provided during the course of the research.
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