Anomaly detection in crowd scene

Anomaly detection in crowd scene is very important because of more concern with people safety in public place. This paper presents an approach to automatically detect abnormal behavior in crowd scene. For this purpose, instead of tracking every person, KLT corners are extracted as feature points to represent moving objects and tracked by optical flow technique to generate motion vectors, which are used to describe motion. We divide whole frame into small blocks, and motion pattern in each block is encoded by the distribution of motion vectors in it. Similar motion patterns are clustered into pattern model in an unsupervised way, and we classify motion pattern into normal or abnormal group according to the deviation between motion pattern and trained model. The results on abnormal events detection in real video demonstrate the effectiveness of the approach.