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In this paper, we propose a tracking algorithm that can robustly handle appearance variations in tracking process. Our method is based on seeds–active appearance model, which is composed by structural sparse coding. In order to compensate for illumination changes, heavy occlusion and appearance self-updating problem, we proposed a mixture online learning scheme for modeling the target object appearance...
In order to achieve good tracking results in single camera environment, the human pose of recognition, based on a skeleton and texture model of video algorithms to identify the body is proposed. Single-camera in the background and complexity of the environment, the target audience can only show 2-D information. The algorithm will be the first skeleton to the human body in accordance with the balance...
In this paper, a novel method based on adaptive particle filter (APF) for tracking human motion in monocular videos is proposed. With an initial human skeleton joint point template, we use the probability density propagation of the particle filers through the model. This algorithm can automatically deal with tracking issues such as occlusion and auto-occlusion. Experimental results from 20 classes...
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