Abnormal behavior detection based on spatial-temporal features

Abnormal behavior detection is an important issue in video surveillance. This paper presents an approach for abnormal behavior detection based on spatial-temporal features. First, the proposed method extracts moving objects from video sequence. Then, it tracks moving objects to detect their overlapping. Finally, a clutter-model is built up based on the changes of spatial-temporal feature to detect abnormal behavior. Experimental results show the effectiveness of the proposed approach.