Video behavior recognition based on locally compressive sensing
WANG Wenfeng1, CHEN Xi1, WANG Haiyang2, PENG Wei3, QIAN Jing4, ZHENG Hongwei1
1. State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China; 2. Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China; 3. School of Information Science and Engineering, Xiamen University, Xiamen 361005, China; 4. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
Abstract：Compressive sensing has been successfully applied in the field of target tracking but not for behavior recognition. This paper presents a locally compressive sensing algorithm for behavior analysis which combines compressive tracking and centroid localization for recognition of video object behavior. Local compressive sensing selects a behavior-sensitive area for compressive tracking which characterizes target behavior based on classification of the object trajectory and local centroid velocity. Tests show that locally compressive sensing can accurately recognize global behavior such as running and falling and local behavior such as smiles and blinking. Therefore, the locally compressive sensing method is of great value that can be used for video surveillance and behavior recognition.
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2018, Vol. 58 
