Temporal-Spatial Coherence Based Abnormal Behavior Detection

To improve the accuracy and speed of the local abnormal detection, a novel method based on Temporal-Spatial Coherence model is proposed. Specifically, the video block is firstly extracted using the gradient histogram and optimized based on the temporal-spatial coherence. Then the normal behavior model and abnormal behavior model is learned via the tensor voting algorithm and the temporal-spatial coherence respectively. Finally, abnormal behavior is detected and labeled. The experiments conducted on the public UCSD and Subway datasets demonstrate the efficiency of the proposed method for local abnormal behavior detection.