Robust Object Tracking using Probabilistic Hypergraph Ranking and Superpixels
Ruitao Lü, Wanying Xu, Yongbin Zheng, Shengjian Bai, Xinsheng Huang · 2016
Online object tracking is a challenging issue because the appearance of an object tends to change due to intrinsic or extrinsic factors.In this study, we propose a robust tracking algorithm based on probabilistic hypergraph ranking and superpixels.The probabilistic hypergraph is constructed by mid-level visual cues and their spatial relationships.Then, the confidence map at mid-level cues is obtained by hypergraph ranking analysis, which takes the high order intrinsic relationships of superpixels into account.Third, Object tracking is formulated as a transductive learning issue, and the optimal target location is determined by maximum a posterior estimation on the ranking scores.Finally, a dynamic updating scheme is proposed to address appearance variations and alleviate tracking drift.A series of experiments and evaluations on various challenging sequences are performed, and the results show that the proposed algorithm performs favorably against other existing state-of-the-art methods.