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In this paper, a novel method of multi-strategy object tracking for video surveillance is proposed. Under this framework, the moving and stationary objects are tracked separately, so that different reliable features for different types of objects can be exploited efficiently. For a moving object, the global color features called dominant color histogram (DCH) are reliable for object tracking. An efficient sequential approach is employed, which first estimates the depth order of the objects using DCH, then tracks each individual one-by-one with mean- shift and exclusion operations. For a stationary object, the image template is accurate for object representation and matching. A layer model is employed for stationary object tracking. Stationary objects are classified as "visible", "occluded", and "removed". For people stop moving in scene, they seldom stay completely motionless. Therefore, two more states, "changing pose", and "start moving" are added. Once the stationary person is detected as "start moving", he will be switched to moving object tracking seamlessly. The proposed method has been successfully applied in a real-time intelligent CCTV surveillance system for unusual event detection and tested in both real-world public sites and public datasets from PETS2006. Very encouraging results have been obtained.