  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 tracking method, which fully exploits
high-order geometric relations among multiple correspondences of parts in
consecutive frames. Then visual tracking is formulated as the mode-seeking
problem on the hypergraph in which vertices represent correspondence hypotheses
and hyperedges describe high-order geometric relations. Besides, a
confidence-aware sampling method is developed to select representative vertices
and hyperedges to construct the geometric hypergraph for more robustness and
scalability. The experiments are carried out on two challenging datasets
(VOT2014 and Deform-SOT) to demonstrate that the proposed method performs
favorable against other existing trackers.
