Visual Analysis of Crowded Scenes

Video Surveillance and Monitoring is very active area of research in Computer Vision. However, most of the current approaches assume that the observed scene is not crowded, and that reliable tracks of objects are available over longer durations. Therefore, these approaches are not extendable to more challenging surveillance videos of crowded environments like markets, subways, religious festivals, parades, concerts, football matches etc, where tracking of individual objects is very hard, if not impossible. In this talk, I will present a framework for modeling scenes involving high density crowds in which Lagrangian particle dynamics are used to segment crowd flows and detect any flow instability. Next, I will discuss an algorithm that tracks an individual within the crowd. The approach is based on the observation that a pedestrian behavior in crowds results from the collective behavioral patterns evolving from the space time interaction of large number of individuals among themselves and with the geometry of the scene. Therefore, we incorporate the influences generated by other individuals of the crowd and scene geometry into the tracking algorithm itself.