Multiple People Tracking Using Non-iterative Tensor Voting Method
نویسنده
چکیده
Human tracking is an emerging research field in computer vision and video surveillance. Lightning changes and occlusion are the main challenges faced during tracking. This paper focuses on people tracking both in indoor and outdoor scenes. Our method is based on color modeling using tensor voting framework, which can handle these challenges. The lightning conditions are controlled by homomorphic filtering and occlusion is handled by modelling the color clothing of each individual. Tensor voting is non-iterative method in which only the scale has to be specified at beginning and is able to build number of clusters automatically. The experimental work shows that our method can track the person successfully and also handle occlusion by finding the centroid of a person.
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تاریخ انتشار 2015