The performance of a particle filter is strongly influenced by the choice of proposal distribution. In order to improve the performance of particle filter for target tracking, a particle filter tracking method based on motion detection is proposed to improve the proposal distribution. A new proposal distribution, which integrates the motion information of the current frame with the prior distribution, is developed. A part of the particles is sampled from the system transition density, and the others from the motion region detected by using the Gauss background modeling. Thus, the prior distribution of particles is determined by both the system transition density and the observations. The experiments show that the method is very effective under the moving background and the occlusion circumstances.