Multiple occlusion target tracking is usually a difficult problem in video surveillance. But in many cases, traditional mean shift tracking algorithms fail to track occlusion targets robustly. In this work, we focus on improving mean shift tracking algorithms to model and track all kinds of occlusion targets in video surveillance scenes. Two primary improvements on traditional mean shift tracking algorithms are proposed. First, after we determine which target the overlapping patches belong to, the nonocclusion part of each occlusion target can be obtained and applied to the tracking algorithm. Second, all the related occlusion target states are iteratively estimated one after another to eliminate the occlusion effects during the tracking process. Furthermore, the contrast experiment results show that the improved algorithm can track multiple occlusion targets, whereas traditional mean shift tracking algorithms fail.