Video Anomaly Detection Algorithm Combining Global and Local Video Representation
HU Zhengping1,2, ZHAO Mengyao1, XIN Bingyi1
1. School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004; 2. Key Laboratory of Information Transmission and Signal Processing of Hebei Province, Yanshan University, Qinhuangdao 066004
Abstract Aiming at the problem of video anomaly detection, a video anomaly detection algorithm combining global and local video representation is proposed. Firstly, the input video continuous multi-frames are divided into video blocks. The video blocks are divided into non-overlapping space-time cubes according to the spatial position. The global spatiotemporal grid position support vector data description (SVDD) model based on spatial position is constructed using the space-time cubes motion features. Then, the local texture motion features are extracted for the moving targets of videos. SVDD algorithm is utilized to obtain the hypersphere boundary around the target features, and the normal behavior model of the moving targets is constructed. Finally, the two parts are combined to conduct more comprehensive detection. Experiments on public datasets verify the effectiveness of the proposed algorithm.
Fund:Supported by General Program of National Natural Science Foundation of China(No.61771420), Natural Science Foundation of Hebei Province(No.F2016203422)
Corresponding Authors:
HU Zhengping, Ph.D., professor. His research interests include pattern recognition.
About author:: ZHAO Mengyao, Ph.D. candidate. Her research interests include video abnormal behavior detection; XIN Bingyi, master student. His research interests include robust pattern recognition technology.
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2020, Vol. 33 
