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http://dx.doi.org/10.7840/kics.2014.39C.8.731

Detection of Crowd Escape Behavior in Surveillance Video  

Park, Junwook (Hanbat National University Dept. of Control and Instrumentation Engineering)
Kwak, Sooyeong (Hanbat National University Dept. of Electronics and Control Engineering)
Publication Information
The Journal of Korean Institute of Communications and Information Sciences / v.39C, no.8, 2014 , pp. 731-737 More about this Journal
Abstract
This paper presents abnormal behavior detection in crowd within surveillance video. We have defined below two cases as a abnormal behavior; first as a sporadically spread phenomenon and second as a sudden running in same direction. In order to detect these two abnormal behaviors, we first extract the motion vector and propose a new descriptor which is combined MHOF(Multi-scale Histogram of Optical Flow) and DCHOF(Directional Change Histogram of Optical Flow). Also, binary classifier SVM(Support Vector Machine) is used for detection. The accuracy of the proposed algorithm is evaluated by both UMN and PETS 2009 dataset and comparisons with the state-of-the-art method validate the advantages of our algorithm.
Keywords
Abnormal behavior; escape behavior detection; intelligent surveillance system;
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Times Cited By KSCI : 2  (Citation Analysis)
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1 A. Adam, E. Rivin, I. Shimshoni, and D. Reintz, "Robust real-time unusual event detection using multiple fixed-location monitors," IEEE Trans. Pattern Anal. Machine Intell., vol. 30, no. 3, pp. 555-560, Mar. 2008.   DOI   ScienceOn
2 H.-S. Park and C.-S. Bae, "Real-time recognition and tracking system of multiple moving object," J. KICS, vol. 36, no. 7, pp. 421-427, Jul. 2011.   DOI
3 G. Bae, "Detection of abnormal behavior by scene analysis in surveillance video," J. KICS, vol. 36, no. 12, pp. 744-752, Dec. 2011.   과학기술학회마을   DOI   ScienceOn
4 T. Jang, Y. Shin, and J. Kim, "A study on the object extraction and tracking system for intelligent surveillance," J. KICS, vol. 38, no. 7B, pp. 589-595, Jul. 2013.   과학기술학회마을   DOI   ScienceOn
5 R. Mehran, A. Oyama, and M. Shah, "Abnormal crowd behavior detection using social force model," IEEE Int. Conf. CVPR, pp. 935-942, Miami, FL, Jun. 2009.
6 Y. Shi, Y. Gao, and R. Wang, "Real-time abnormal event detection in complicated scenes," Int. Conf. Pattern Recognition (ICPR), pp. 3653-3656, Istanbul, Aug. 2010.
7 S. Wu, H.-S. Wong, and Y. Zhiwen, "A bayesian model for crowded escape behavior detection," IEEE Trans. Circuits and Syst. for Video Technol., vol. 24, no. 1, pp. 85-98, Jan. 2014.   DOI   ScienceOn
8 V. Vapnik, The nature of statistical learning theory, 2nd Ed., NY: Springer-verlag, 1995.
9 UMN dataset, http://mha.cs.umn.edu/proj_events.shtml#crowd
10 PETS 2009 dataset, http://pets2009.net/
11 G. Farneback, "Two-frame motion estimation based on polynomial expansion," Lecture Notes in Comput. Sci., vol. 2749, pp. 363-370, Jun. 2003.
12 S. S. Pathan, A. Al-Hamadi, and B. Michaelis, "Incorporating social entropy for crowd behavior detection using SVM," Advances in Visual Computing, Springer Berlin Heidelberg, pp. 153-162, 2010.
13 D.-Y. Chen and P.-C. Huang, "Motion-based unusual event detection in human crowds," J. Visual Commun. Image Representation, vol. 22, no. 2, pp. 178-186, 2012.
14 S. Wu, B. E. Moore, and M. Shah, "Chaotic invariants of lagrangian particle trajectories for anomaly detection in crowded scenes," IEEE Conf. Computer Vision and Pattern Recognition (CVPR), pp. 2054-2060, San Francisco, CA, Jun. 2010.