A multi-feature vision tracking method by CamShift optimizing particle filter was proposed. The position and scale of the particle were optimized by CamShift, and the algorithm can select the proper size of the tracking window in the scenarios that the object scale varies. Meanwhile, by adopting an adaptive method, the color information was combined with motion information in the framework of CamShift optimizing particle filter. Because the propagation of the particle was optimized, which could make each particle converge on the true state region of target, the effectiveness of particle is improved. The experimental results show that the tracking error of CamShift optimizing particle filter with only 10 particles is less than that of the traditional particle filter with 100 particles. What's more, as a result of the use of multi-feature, it can still track the target accurately and solve the problems encountered in tracking an object with illumination variation and the background color clutter. Stable tracking can be realized by a few particles, which can reduce calculation cost and improve the robustness of tracking.