To improve the accuracy and robustness of occlusions and fast moving in video target tracking, a tracking algorithm based on particle filter optimized by a new cloud adaptive particle swarm optimization（CAPSO） is proposed. The possible position of moving target in the next frame image is predicted by particle filter, and the target template and candidate regions are mateched with the color histogram statistical characteristics to ensure the tracking accuracy. Then the proposed CAPSO is utilized to divide the particles into three group based on the fitness of the particle in order to adopt different inertia weight generating strategy. The inertia weight in general group is adaptively varied depending on X-conditional cloud generator. The inertia weight has randomness property because of the cloud model. Therefore, the re-sampling frequency of particles filter is reduced. The computational cost of particle filter is effectively reduced and it is effective to solve the target tracking problem of occlusions. In addition, the algorithm can effectively balance the global and local searching abilities of the algorithm by adopting three different inertia weight generating strategies, which can adjust the particle search range, thus being adaptable to different motion levels. Experimental results show that the proposed algorithm has a good tracking accuracy and real-time performance in case of occlusions and fast moving in video target tracking.