A new occluded object tracking algorithm based on mirror image and Mean Shift was proposed to solve the problem that the track object is not accurate, even lost during full occlusion in this paper. The algorithm included three steps: Firstly, when the object was uncovered (Bhattacharyya coefficient matching degree of adjacent frames was greater than or equal to 80%), color features and contour features were used to locate the target, and size adaptive adjustment was realized by sandbag kernel window based on partition. Secondly, when the object is occluded (Bhattacharyya coefficient matching degree of adjacent frames was less than 80%), the location and the size of the target was predicted by using prior training classifier and mirror principle.Thirdly, When target left the occlusion area (Bhattacharyya coefficient matching degree of adjacent frames was greater than or equal to 80% again), Mean Shift algorithm was used to track the target. The experimental results show that when the object is fully occluded, the proposed algorithm is more accurate and robust to better solve the occlusion problem than sub-regional on-line Boosting algorithm and multi-view object tracking algorithm combining modified fusion feature with dynamic occlusion threshold, and meets the real-timer requirements.