50120140502009

International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),

ISSN 0976 - 6375(Online), Volume 5, Issue 2, February (2014), pp. 71-87 © IAEME

71

CROWD BEHAVIOUR ANALYSIS CONSIDERING INTER-PERSONNEL

ACTIVITIES IN SURVEILLANCE SYSTEMS

Najmuzzama Zerdi1, Dr. Subhash S Kulkarni

2, Dr. V. D. Mytri

3, Kashyap D Dhruve

4

1(Professor and Head, Dept. of E&C, K.C.T.E.C Gulbarga- 585101, Karnataka, India)

2(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

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ISSN 0976 – 6367(Print)

ISSN 0976 – 6375(Online)

Volume 5, Issue 2, February (2014), pp. 71-87

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72

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



73

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 angle-

of-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

Figure 1: model overview

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

computed frame wise. Therefore, the streak flow is derived which is used for analysis. For

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

detected. The proposed modeling is illustrated in Figure 1.

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 .



75

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

domain definition difference between and . The optical flow representation of the

frame which is defined over the window size instead of

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 and is represented as

From the first order expansion about the point instead of Equation 4

can described as

Therefore, the is the temporal frame image derivative at the point . The

point is described as



76

The derivative at is denoted as

From Equation 5we can denote , is the gradient vector and can be defined as

The derivatives and can be evaluate directly from the image

in the , which is a neighborhood of the point independently

from the next image . The derivative images satisfy the expression

and can be represents as

Based on and defined above the computation of is given as

Equation 12 can be further simplified and written as:

Where



77

As and matrix is invertible, then the optical flow vector is defined as

Based on the above equation we can observed that , it is evident that contains

information of the gradients in the and direction of . A large number of iterations have to be

considered to obtain accurate optical flow of personnel in the crowd video frames. Let represent

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 the residual pixel trajectory vector

and mismatch vectors are obtained. The residual vector minimizes the error

function , defined as

Using Equation 14 the solution of can be obtained and is defined as

Where the mismatch vector matrix is given as

In Equation 16 represents the frame difference and is defined as



78

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

computed at steps. That vector is the amount of residual difference between the video frames

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

The iteration steps to achieve convergence are repeated till the value of is below a preset

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 direction at the time instance can

be represented as

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



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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

and forming extended particles. To compute , and have to be computed. The computation of

and are similar in nature. If then let us consider a vector to obtain the

streak flow in the direction. The extended particle has three pixels as its neighbors which forms

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, represents the basis function.

Using the interpolation method the parameters and are obtained. For all the

vectors in and based on Equation 26 we can state

Then, are the elements of the matrix . Therefore, following equation, 25 and 26

similarly can be computed. Using and the streak flow is acquired. In Graph theoretic

approach based Crowd Behavior Analysis and Classification System model use of streak

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



80

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

Based on the above equation we can denoted that represents the number of vectors

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



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However, the local differences in the sub sets are measured to observed the finer movements

in the graphs and is computed using

During the process is a function that defines the matching between the sub

sets and . Combining all the above definitions the variance between two frames represented

as graphs and calculated as

Where is a predefined integer and the cumulative variance noticed till the frame can

be given as

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 ( )



82

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

compared to the VFF method. The misclassification ratio for is 0.18 and for GCBACS is 0.5.

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.



83

Figure 3: Curves for Crowd Activity Classification based on and for Case 1

Figure 4: Area under Curve considering and for Case 1

Figure 5: Classification Error considering and for Case 1



84

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 and for Case 2



85

0

0.2

0.4

0.6

0.8

1

1.2

0

AR

EA

ALGORITHM NAME

AREA UNDER ROC CURVE

GCBACS

VFF

Figure 8: Area under Curve considering and for Case 2

0

0.2

0.4

0 1 2 3 4 5

CL

AS

SIF

ICA

TIO

N E

RR

OR

CLASSIFICATION ERROR

GCBACS VFF

Figure 9: Classification Error considering and 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



86

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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