Open Access Journal

ISSN : 2394-6849 (Online)

International Journal of Engineering Research in Electronics and Communication Engineering(IJERECE)

Monthly Journal for Electronics and Communication Engineering

Open Access Journal

International Journal of Engineering Research in Electronics and Communication Engineering(IJERECE)

Monthly Journal for Electronics and Communication Engineering

ISSN : 2394-6849 (Online)

Human Crowd Anomaly Detection for Video Surveillance

Author : Shreekar Waghela 1 Shreedarshan K 2

Date of Publication :3rd August 2017

Abstract: Video surveillance is very essential as threat and crime increases. In this project, a method is proposed in which the behaviour of crowd is detected without individual tracking of objects in a frame. This method is based on the motion intensity of the crowd which can be determined by accumulating all optical flow vectors of a frame. The abnormal crowd activity can then be detected by setting up a threshold to detect any sudden change in motion intensity.

Reference :

  1. [1] H. Mousavi, S. Mohammadi, Alessandro Perina, R. Chellali, Vittorio Murino,“Analyzing Tracklets for the Detection of Abnormal Crowd Behaviour”, IEEE Winter Conference on Applications of Computer Vision, 2015.

    [2] B.Yogameena, K.S. Priya, “ Synoptic Video Based Human Crowd Behaviour Analysis for Forensic Video Surveillance”, IEEE International Conference on Advances in Pattern Recognition(ICAPR), 2015.

    [3] Aravinda Rao, J. Gubbi, M. Slaven , M. Palaniswami, “Crowd Event Detection on Optical Flow Manifolds”, IEEE transactions on cybernetics, 2016

    [4] Yang , Xia Li and Limin Jia, “Abnormal Crowd Behavior Detection Based on Optical Flow and Dynamic Threshold”, IEEE International Conference on Intelligent Control and Automation, 2014.

    [5] B. Solmaz, Brian E. Moore and Mubarak Shah, “Identifying Behaviors in Crowd Scenes Using Stability Analysis for Dynamical Systems”, IEEE Transactions on pattern analysis and machine intelligene, 2010.

    [6] Guo Xiong, Xinyu Wu, Yen-Lun Chen and Yong Ou, “Abnormal Crowd Behavior Detection Based on the Energy Model”, IEEE International Conference on Information and Automation, 2011.

    [7] Zhi Zhong, Weizhong Ye, “Crowd Energy and Feature Analysis”, IEEE International Conference on Integration Technology, 2007.


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