The probability hypothesis density filter based multi-target visual tracking

The issue of tracking a variable number of multiple targets is discussed in this paper. The theory in relation to probability hypothesis density (PHD) filter is given firstly. We present the motion detection, dynamic equation, measurement equation and visual multi-target tracking algorithm based on Gaussian mixture probability hypothesis density (GM-PHD) in details. The proposed method can track objects correctly when they appear, merge, split and disappear in the field of view of a camera. Our experimental results show that GM-PHD based multi-target visual tracking is robust in clutter and could effectively track a varying number of targets.