Visual Tracking via Probabilistic Hypergraph Ranking

Abstract

Online object tracking is a challenging issue because the appearance of an object tends to change due to intrinsic or extrinsic factors. In this paper, we propose a tracking algorithm based on probabilistic hypergraph ranking. First, three types of hypergraphs are constructed to encode local affinity information. Then, a probabilistic hypergraph is built by combining three distinct hypergraphs linearly. Second, an adaptive template constraint is proposed to effectively use the discriminative information of different templates. Third, object tracking is formulated as a transductive learning issue, and the optimal target location is determined by maximum a posteriori estimation on the ranking scores. Finally, a dynamic updating scheme of positive and negative template sets provides the proposed tracker with robustness against appearance variations. A series of experiments and evaluations on various challenging image sequences is performed, and the results show that the proposed algorithm performs favorably against other state-of-the-art methods.