Object tracking is one of the hottest topics in computer vision community. It has been widely applied in video surveillance, human-computer interface, intelligent transportation and so on. Although research on object tracking has been conducted for several decades, there are still many challenges and difficulties, for example, occlusion, clutter background, complex scenes and object appearance and shape changes. In this dissertation, novel object appearance model and motion constraint are employed to improve the robustness of kernel based tracking method in the cases of occlusion, clutter background and object complex motion; shape prior is exploited in contour tracking to deal with occlusion and object shape changes; visual attention model is introduced into object tracking to enhance the difference between object and background, which provides effective prior information for object tracking. The main contribution of this dissertation is as follows. 1.A robust mean shift object tracking algorithm via fragment based representation is proposed to compensate for the spatial information loss in histogram. The spatial information of the object is reserved in the fragment based representation, which makes tracking much more robust especially when the object is partially occluded or there are attractive objects in the background. Meanwhile, a mean shift object tracking algorithm integrating local mode seeking is proposed to deal with the problem that mean shift is sensitive to initialization. The proper modes are chosen in the object region and local mode seeking is used to initialize the mean shift iteration, which makes tracking much more robust even when the object is moving fast. Finally, combining the above two algorithm can make tracking even more robust in complex natural scenes. 2.A multiple collaborative kernel tracking algorithm with cross ratio invariant constraint is presented. The complex motion of object is decomposed into simple motions using multi-kernel strategy and the cross ratio invariant constraint improves the observability of the system. Object under complex motion can still be tracked by the algorithm. 3.We present a novel contour tracking approach that combines particle filter and 3D graph cut. Particle filter predicts the location of the object while 3D graph cut segment the object near the predicted location. Traditional histogram based particle filter does not reserve the spatial information and is easy to be affected by the backg...
修改评论