Finding Highly Frequented Paths in Video Sequences

We propose a novel algorithm to find highly frequented paths of motion trajectories obtained from video sequences. This is achieved by representing the motion trajectories in the scene as sequences of prototypes obtained by a combined vector quantization and growing neural gas algorithm. In contrast to existing methods, the proposed algorithm can be applied to data sets containing motion trajectories of varying length. The algorithm does not assume an a priori fixed number of prototypes. We demonstrate results on surveillance video sequences of cars driving on a highway and pedestrians walking in a major railway station.