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Geometric Hypergraph Learning for Visual Tracking
[article]
2016
arXiv
pre-print
Graph based representation is widely used in visual tracking field by finding correct correspondences between target parts in consecutive frames. However, most graph based trackers consider pairwise geometric relations between local parts. They do not make full use of the target's intrinsic structure, thereby making the representation easily disturbed by errors in pairwise affinities when large deformation and occlusion occur. In this paper, we propose a geometric hypergraph learning based
arXiv:1603.05930v1
fatcat:7p7styik6redzaalvpay6jjaa4