Abstract: Particle filter simulates posterior probability distribution of tracking target through a collection of random particles. Whereas the interferences of analogues occur frequently in a normal condition, random particles might not be able to proximate target state that easily. This study presents an innovative particle tracking method, with a combination of color and motion information provided by improved Camshift. First, the centroid position of tracking target is denoted by joint probability distribution of color histogram and motion information, such that the stability of Camshift has been improved. Then, an improved Camshift is introduced to optimize particles evolution. With analogue interferences, grey prediction model initialized by Camshift is imposed to harvest proposal distribution of optimized particle filter. Finally, in the sampling process, particles are sampled hierarchically to denote exact target position on the basis of the weights. Experimental tasks have demonstrated that the method performs well under the condition of targets maneuvering, incomplete or complete occlusions. Furthermore, it outperformed the previous with more robustness and computation accuracy.