Crowd Behavior Analysis and Classification using Graph Theoretic Approach

Najmuzzama Zerdi; Dr. Subhash S Kulkarni; Kashyap D Dhruve

Crowd Behavior Analysis and Classification using Graph Theoretic Approach

Najmuzzama Zerdi * §

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Dr. Subhash S Kulkarni

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Kashyap D Dhruve

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Crowd Behavior Analysis and Classification using Graph Theoretic Approach

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Abstract

Surveillance systems are commonly used for security and monitoring. The need to automate these systems is well understood. To address this issue we introduce the Graph theoretic approach based Crowd Behavior Analysis and Classification System (GCBACS). The crowd behavior is observed based on the motion trajectories of the personnel in the crowd. Optical flow methods are used to obtain the streak lines and path lines of the crowd personnel trajectories. The streak flow is constructed based on the path and streak lines. The personnel and their respective potential vectors obtained from the streak flows are used to represent each frame as a graph. The frames of the surveillance videos are analyzed using graph theoretic approaches.

References

24 Cites in Article

Reference Format

Hang Su,; Hua Yang,; Shibao Zheng,; Yawen,Fan,Sha Wei (2013). The Large-Scale Crowd Behavior Perception Based on Spatio-Temporal Viscous Fluid Field.
I Haritaoglu,D Harwood,L Davis (2000). W/sup 4/: real-time surveillance of people and their activities.
Ying Xin,; Guangmin Sun,; Qiang Wu (1693). A preprocessing method for tracking and counting pedestrians in bus video monitor.
P Padeleris,X Zabulis,A Argyros (2013). Multicamera tracking of multiple humans based on colored visual hulls.
S Ali,M Shah (2007). A Lagrangian particle dynamics approach for crowd flow segmentation and stability analysis.
Yunqian Ma,Petr Cisar (2008). Activity Representation in Crowd.
S Ali,M Shah (2008). Floor fields for tracking in high density crowd scenes.
Y Yang,J Liu,M Shah (2009). Video scene understanding using multi-scale analysis.
E Andrade,R Fisher,S Blunsden (2006). Modelling crowd scenes for event detection.
Louis Kratz,Ko Nishino (2009). Anomaly detection in extremely crowded scenes using spatio-temporal motion pattern models.
Ramin Mehran,Alexis Oyama,Mubarak Shah (2009). Abnormal crowd behavior detection using social force model.
Jaechul Kim,Kristen Grauman (2009). Observe locally, infer globally: A space-time MRF for detecting abnormal activities with incremental updates.
Prazdny (1983). On the information in optical flows.
S Ali,M Shah (2007). A Lagrangian Particle Dynamics Approach for Crowd Flow Segmentation and Stability Analysis.
R Mehran,B Moore,M Shah,Ldquo (2010). A Streakline Representation of Flow in Crowded Scenes,&rdquo.
X Wang,X Ma,W Grimson (2008). Unsupervised activity perception in crowded and complicated scenes using hierarchical bayesian models.
Hang Su,Hua Yang,Shibao Zheng,Yawen Fan,Sha Wei (2013). The Large-Scale Crowd Behavior Perception Based on Spatio-Temporal Viscous Fluid Field.
Si Wu,Hau-San Wong,Zhiwen Yu (2014). A Bayesian Model for Crowd Escape Behavior Detection.
Briane Berkansolmaz,Mubarakshah (2012). Identifying Behaviors in Crowd Scenes Using Stability Analysis for Dynamical Systems.
Duan-Yu Chen,Po-Chung Huang (2013). Visual-Based Human Crowds Behavior Analysis Based on Graph Modeling and Matching.
Norman Nuriapelechano,Badler (2006). Modeling Crowd and Trained Leader Behavior during Building Evacuation.
T Corpetti,E Mémin,P Pérez (2003). Extraction of Singular Points from Dense Motion Fields: An Analytic Approach.
(null). University of Minnesota (UMN) Map Server.
A Wald (1950). Statistical decision functions.

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Najmuzzama Zerdi, Dr. Subhash S Kulkarni, Kashyap D Dhruve. 2014. "Crowd Behavior Analysis and Classification using Graph Theoretic Approach". Global Journal of Computer Science and Technology - F: Graphics & Vision GJCST-F Volume 14 (GJCST Volume 14 Issue F1).

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GJCST Volume 14 Issue F1

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

abnormal crowd behavior crowd motion Graph Theory normal optical flow path lines streak line flow streak lines threshold video surveillance

Classification

GJCST-F Classification G.2.2

Submission ReceivedDecember 7, 2013
Peer Review Double Blind
Handling Editor
Accepted December 31, 2013
Published January 15, 2014

Version of record

v1.2

Issue date

March 28, 2014

Language

English

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Article Matrices

Total Score: 154

Country: India

Subject: Global Journal of Computer Science and Technology

Authors: Najmuzzama Zerdi, Dr. Subhash S Kulkarni, Dr.V .D. Mytri, Kashyap D Dhruve (PhD/Dr. count: 2)

View Count (all-time): 440

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Publish Date: 2014 01, Wed

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Total Views: 3.7K

Total Downloads: 184

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