Abnormal behavior detection based on the motion-changed rules

Abnormal activity will lead to the uncommon changes in the crowd behavior. In other words, the crowd motion changes conforms to certain rules for valid behaviors, while for abnormal events the motion changes are uncontrolled. For this, this paper discovers the motion-changed rules to detect and localize abnormal behavior in crowd videos. Specifically, we first generate the motion patterns based on the descriptor of collectiveness. Then each frame pair is represented as a transfer matrix whose elements are the difference of a set of motion patterns. Thereafter, the motion-changed rules are constructed in the transformation space using bag-of-words approach. Finally, the proposed approach measures the similarity between motion-changed rules and the incoming video data in order to examine whether the actions are anomalous. The approach is tested on the UMN dataset and a challenging dataset of crowd videos taken from the railway station. The experimental results demonstrate the effectiveness of the proposed method for detection the abnormal behavior.