Journal Papers

Liu, Risheng

Liu, RS (reprint author), Dalian Univ Technol, Sch Software Technol, Dalian, Liaoning Provin, Peoples R China.; Liu, RS (reprint author), Key Lab Ubiquitous Network & Serv Software Liaoni, Dalian, Liaoning Provin, Peoples R China.

Wang, Jing,Shang, Xiaoke,Wang, Yiyang,Su, Zhixun,Cai, Yu

2016-12-16

PLOS ONE

SCIE、PubMed、Scopus

J

11

12

e0168093

1932-6203

In this paper, we propose a novel sparse coding and counting method under Bayesian framework for visual tracking. In contrast to existing methods, the proposed method employs the combination of L-0 and L-1 norm to regularize the linear coefficients of incrementally updated linear basis. The sparsity constraint enables the tracker to effectively handle difficult challenges, such as occlusion or image corruption. To achieve real-time processing, we propose a fast and efficient numerical algorithm for solving the proposed model. Although it is an NP-hard problem, the proposed accelerated proximal gradient (APG) approach is guaranteed to converge to a solution quickly. Besides, we provide a closed solution of combining L-0 and L-1 regularized representation to obtain better sparsity. Experimental results on challenging video sequences demonstrate that the proposed method achieves state-of-the-art results both in accuracy and speed.