The Probability Hypothesis Density Filter Based Multi-target Visual Tracking

【摘要】：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.