Event based surveillance video synopsis using trajectory kinematics descriptors

Abstract

Video synopsis has been shown its promising performance in visual surveillance, but the rearranged foreground objects may disorderly occlude to each other which makes end users hard to identify the targets. In this paper, a novel event based video synopsis method is proposed by using the clustering results of trajectories of foreground objects. To represent the kinematic events of each trajectory, trajectory kinematics descriptors are applied. Then, affinity propagation is used to cluster trajectories with similar kinematic events. Finally, each kinematic event group is used to generate an event based synopsis video. As shown in the experiments, the generated event based synopsis videos can effectively and efficiently reduce the lengths of the surveillance videos and are much clear for browsing compared to the states-of-the-art video synopsis methods.