Applying filters to repeating motion based trajectories for video classification

Abstract

The presented video classification system is based on the trajectory of repeating motion in video scenes. Further on this trajectory has a certain direction and velocity at each time frame. As the position, direction and velocity of the motion trajectory evolve in time, we consider these as motion functions. Later on we transform these functions by FFT and receive frequency domains, which then represent the frequencies of repeating motion. Moreover these frequencies serve as features during classification phase. Our current work focuses on filtering the functions based on the motion’s trajectory in order to reduce noise and emphasize significant parts.