Given that the robustness of traditional particle filter algorithm for target tracking was not very good, especially in environment migration, occlusion and pose variation, an improved particle filter target tracking algorithm was proposed. When establishing the target model, the target’s HSV color feature and Uniform LBP texture feature were weighted and fused. In the process of particle resampling, the weighted random sampling method was adopted, considering the particle’s weight as the impact factor of the resampling rather than determinant in order to magnificently improve the diversity of particles and reduce the adverse effects of particles decay. In the case where the target was disturbed, the Kalman filter was used to offset the target position to obtain the correct position of the target. Finally, the introduction of template updating strategy was combined to update target template. The experimental results showed that compared with the traditional particle filter algorithm and CMT algorithm, the proposed algorithm was robust to occlusion and pose variation in complex environments.