Kernel-based object tracking via particle filter and mean shift algorithm

Abstract

One of the critical tasks in object tracking is the tracking of fast-moving object in random motion, especially in the field of machine vision applications. An approach towards the hybrid of particle filter (PF) and mean shift (MS) algorithm in visual tracking is proposed. In this proposed system, complete occlusion and random movement of object can be handled due to its ability in predicting the object location with adaptive motion model. In addition, the PF is capable to maintain multiple hypotheses to handle clutters in background and temporary failure. However PF requires a large number of particles to approximate the true posterior of the target dynamics. Therefore, MS algorithm is applied to the sampling process of the PF to move these particles in gradient ascent direction. Consequently a small sample size will be sufficient to represent the system dynamics accurately. The proposed approach is aimed to track the moving object in random directions under varying conditions with acceptable computational time.