In order to avoid the poor robustness based on most of present observation models in small sample space, a particle filter algorithm for visual tracking based on partial feature combination is proposed. Partial features can represent the detail of target template effectively, and can alleviate the affection of object deformation, illumination change and partial occlusion in feature matching. The proposed method employs the idea of mixture of Gaussian and uses multiple modes to represent valid partial observation information. The strategy of fusion is more precise and reliable, thus can overcome the degeneracy problem by new measurement and improve the efficiency of object tracking. The small sample space can reduce quantity of particle and computational load in a certain extent. Experimental results indicate the proposed method is more effective than tracking algorithm with single feature or common multi-features fusion, and it has good performance in complex scene.