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.