A Robust Method To Identify Overlapping Crowd Motion Patterns

Abstract

Due to recent advances in new camera technologies and the Internet, millions of videos can be easily accessed from any place at any time. A significant amount of these videos are for surveillance, and include actors such as humans and vehicles performing different actions in dynamic scenes. The goal of this study is to analyze human crowd motions in videos. More specifically, moving humans are tracked throughout a video sequence, and the collective crowd motions are then clustered using path similarities via the Dominant Sets method. While obtaining path similarities, they have also been subjected to many middle steps such as extracting a sparse subset, obtaining transitive closure between motions and selecting popular paths with respect to specific parameters. Hence, we ensure that we obtain a pure and accurate structure information for each cluster. Moreover, we calculate a scalar value, which represents a coherency information for crowds in different scenes. We also compare our method with another state of the art method and show our quantitative and qualitative results obtained from comparison.