Group tracking and behavior recognition in long video surveillance sequences

This paper makes use of recent advances in group tracking and behavior recognition to process large amounts of video surveillance data from an underground railway station and perform a statistical analysis. The most important advantages of our approach are the robustness to process long videos and the capacity to recognize several and different events at once. This analysis automatically brings forward data about the usage of the station and the various behaviors of groups in different hours of the day. This data would be very hard to obtain without an automatic group tracking and behavior recognition method. We present the results and interpretation of one month of processed data from a video surveillance camera in the Torino subway.