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. Then the motion detection, dynamic equation, measurement
equation and visual multi-target tracking algorithm based on Gaussian mixture probability hypothesis density(GM-PHD) are
presented in details. The proposed method can track objects correctly when they appear, merge, split and disappear in the
field of view of a camera. Experimental results show that GM-PHD based multi-target visual tracking is robust in clutter and
can effectively track a varying number of targets.