The invention relates to a moving object tracking method based on multi-feature fusion, belonging to the field of computer vision. At first, in that first frame image, the target area is initialized,and two position filters are respectively train by using the direction histogram and the color features; secondly, the detection samples of two features are extracted around the target in the subsequent frame, and the correlation scores between the two detection samples and the position filters trained in the previous step are calculated respectively, that is to say, the response diagrams of different features are obtained. Thirdly, according to the peak sidelobe ratios of different characteristic response diagrams, the two characteristic response values are weighted and fused, and the point with the largest response value is selected as the current center position of the target. Then the scale pyramid training scale filter is constructed by using the directional gradient histogram feature, and the maximum response point is obtained as the current scale of the target. Finally, according to the peak-to-side ratio of the final response graph of each frame, whether occlusion occurs or notis judged. In the case of occlusion, the position filter is not updated.