Detection of global abnormal events in crowded scenes

In this paper, we propose a novel method for global abnormal events detection in crowded scenes. Each video is described as the set of overlapping space-time cubes. The histogram of optical flow orientation and motion magnitude are used as global feature descriptor to capture the motion magnitude and orientation of the normal and abnormal events. The motion-rich space-time cubes are selected to enhance the computational speed of the algorithm and omit background subtraction step, forms the proposed approach novel and unique. Simple and cost-effective one-class SVM classifier is used to distinguish between normal and abnormal events in a video. The one-class SVM classifier is trained with normal events from the training data. The proposed method is tested on benchmark UMN and PETS2009 dataset. The improved results are reported to evaluate the performance of the proposed method.

[1]  Brian C. Lovell,et al.  Improved anomaly detection in crowded scenes via cell-based analysis of foreground speed, size and texture , 2011, CVPR 2011 WORKSHOPS.

[2]  Sergio A. Velastin,et al.  Crowd analysis: a survey , 2008, Machine Vision and Applications.

[3]  Berthold K. P. Horn,et al.  Determining Optical Flow , 1981, Other Conferences.

[4]  Ramin Mehran,et al.  Abnormal crowd behavior detection using social force model , 2009, CVPR.

[5]  V. Vapnik.  Pattern recognition using generalized portrait method , 1963 .

[6]  Mubarak Shah,et al.  Chaotic invariants of Lagrangian particle trajectories for anomaly detection in crowded scenes , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[7]  Martin D. Levine,et al.  Online Dominant and Anomalous Behavior Detection in Videos , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[8]  Hichem Snoussi,et al.  Histograms of Optical Flow Orientation for Visual Abnormal Events Detection , 2012, 2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance.

[9]  Nuno Vasconcelos,et al.  Anomaly detection in crowded scenes , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[10]  Gian Luca Foresti,et al.  Trajectory-Based Anomalous Event Detection , 2008, IEEE Transactions on Circuits and Systems for Video Technology.

[11]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[12]  Junsong Yuan,et al.  Sparse reconstruction cost for abnormal event detection , 2011, CVPR 2011.

[13]  Anthony Widjaja,et al.  Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , 2003, IEEE Transactions on Neural Networks.

[14]  Aggelos K. Katsaggelos,et al.  Anomalous video event detection using spatiotemporal context , 2011 .

[15]  Hichem Snoussi,et al.  Detection of Abnormal Visual Events via Global Optical Flow Orientation Histogram , 2014, IEEE Transactions on Information Forensics and Security.

[16]  Hichem SNOUSSI,et al.  Detection of Visual Abnormal Events via One-class SVM , 2012 .

[17]  R. Venkatesh Babu,et al.  Real time anomaly detection in H.264 compressed videos , 2013, 2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG).

[18]  Bingbing Ni,et al.  Crowded Scene Analysis: A Survey , 2015, IEEE Transactions on Circuits and Systems for Video Technology.