Abnormal global and local event detection in compressive sensing domain

Abnormal event detection, also known as anomaly detection, is one challenging task in security video surveillance. It is important to develop effective and robust movement representation models for global and local abnormal event detection to fight against factors such as occlusion and illumination change. In this paper, a new algorithm is proposed. It can locate the abnormal events on one frame, and detect the global abnormal frame. The proposed algorithm employs a sparse measurement matrix designed to represent the movement feature based on optical flow efficiently. Then, the abnormal detection mission is constructed as a one-class classification task via merely learning from the training normal samples. Experiments demonstrate that our algorithm performs well on the benchmark abnormal detection datasets against state-of-the-art methods.Abnormal event detection, also known as anomaly detection, is one challenging task in security video surveillance. It is important to develop effective and robust movement representation models for global and local abnormal event detection to fight against factors such as occlusion and illumination change. In this paper, a new algorithm is proposed. It can locate the abnormal events on one frame, and detect the global abnormal frame. The proposed algorithm employs a sparse measurement matrix designed to represent the movement feature based on optical flow efficiently. Then, the abnormal detection mission is constructed as a one-class classification task via merely learning from the training normal samples. Experiments demonstrate that our algorithm performs well on the benchmark abnormal detection datasets against state-of-the-art methods.

[1]  Dimitris Achlioptas,et al.  Database-friendly random projections: Johnson-Lindenstrauss with binary coins , 2003, J. Comput. Syst. Sci..

[2]  Bernhard Schölkopf,et al.  Estimating the Support of a High-Dimensional Distribution , 2001, Neural Computation.

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

[4]  Sotirios Chatzis,et al.  Robust Visual Behavior Recognition , 2010, IEEE Signal Processing Magazine.

[5]  Ehud Rivlin,et al.  Robust Real-Time Unusual Event Detection using Multiple Fixed-Location Monitors , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Quoc Cuong Pham,et al.  Crowd Behavior Analysis Using Local Mid-Level Visual Descriptors , 2017, IEEE Transactions on Circuits and Systems for Video Technology.

[7]  R. DeVore,et al.  A Simple Proof of the Restricted Isometry Property for Random Matrices , 2008 .

[8]  Venkatesh Saligrama,et al.  Abnormality detection using low-level co-occurring events , 2011, Pattern Recognit. Lett..

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

[10]  Haroon Idrees,et al.  Detecting Humans in Dense Crowds Using Locally-Consistent Scale Prior and Global Occlusion Reasoning , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Hichem Snoussi,et al.  Internal Transfer Learning for Improving Performance in Human Action Recognition for Small Datasets , 2017, IEEE Access.

[12]  Ákos Utasi,et al.  Detection of unusual optical flow patterns by multilevel hidden Markov models , 2010 .

[13]  Jie Chen,et al.  Online Least Squares One-Class Support Vector Machines-Based Abnormal Visual Event Detection , 2013, Sensors.

[14]  Junsong Yuan,et al.  Abnormal event detection in crowded scenes using sparse representation , 2013, Pattern Recognit..

[15]  Yandong Tang,et al.  Video Anomaly Search in Crowded Scenes via Spatio-Temporal Motion Context , 2013, IEEE Transactions on Information Forensics and Security.

[16]  Michael G. Strintzis,et al.  Swarm Intelligence for Detecting Interesting Events in Crowded Environments , 2015, IEEE Transactions on Image Processing.

[17]  Nuno Vasconcelos,et al.  Anomaly Detection and Localization in Crowded Scenes , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Imran N. Junejo,et al.  Social network model for crowd anomaly detection and localization , 2017, Pattern Recognit..