Study on Anomaly Detection in Crowd Scene

Anomaly detection technology in crowd scene is very important in public place. Crowd detection differs from pedestrian detection which we assume no individual pedestrian can be properly segmented in the image. We propose a scheme which the scen can be treated the crowd motion patterns as the spatial-temporal domain. In the classification stage, we divide whole frame into small blocks, and motion pattern in each block is encoded by the distribution of motion bags in it. PLSA classifier is proposed to infer classification of crowed detection, and we classify motion pattern into normal or abnormal group according to the deviation between motion pattern and train model. The comprehensive implementation can detect crowd in real-time. This paper presents an approach to automatically detect abnormal behavior in crowd scene with Interest points to represent moving objects to generate word of bags, which are used to describe crowed moriment results show that the speed of detection has been greatly improved using our approach.