Clustering of local behaviour in crowd videos

Surveillance cameras are playing more important role in our daily life with the increasing number of human population and surveillance cameras. While there are a myriad of methods for video analysis, they are generally designed for low-density areas. Running of these algorithms in crowded areas would not give expected results and results in high number of false alarms giving rise to a need for different approaches for crowded area surveillance. Due to occlusions and images of individuals having a low resolution, holistic approaches have started to be preferred rather than detection and tracking of individuals. In this work, a method based on detection of regional behaviors in high density crowds is proposed. The method clusters the crowd behavior in different areas of the scene and can be used as a basis for anomaly detection.