title: Adaptive low-rank subspace learning with online optimization for robust visual tracking.
abstract: In recent years, sparse and low-rank models have been widely used to formulate appearance subspace for visual tracking. However, most existing methods only consider the sparsity or low-rankness of the coefficients, which is not sufficient enough for appearance subspace learning on complex video sequences. Moreover, as both the low-rank and the column sparse measures are tightly related to all the samples in the sequences, it is challenging to incrementally solve optimization problems with both nuclear norm and column sparse norm on sequentially obtained video data. To address above limitations, this paper develops a novel low-rank subspace learning with adaptive penalization (LSAP) framework for subspace based robust visual tracking. Different from previous work, which often simply decomposes observations as low-rank features and sparse errors, LSAP simultaneously learns the subspace basis, low-rank coefficients and column sparse errors to formulate appearance subspace. Within LSAP framework, we introduce a Hadamard production based regularization to incorporate rich generative/discriminative structure constraints to adaptively penalize the coefficients for subspace learning. It is shown that such adaptive penalization can significantly improve the robustness of LSAP on severely corrupted dataset. To utilize LSAP for online visual tracking, we also develop an efficient incremental optimization scheme for nuclear norm and column sparse norm minimizations. Experiments on 50 challenging video sequences demonstrate that our tracker outperforms other state-of-the-art methods.
authors.affiliation: School of Software Technology, Dalian University of Technology, Dalian, 116024, China; The State Key Laboratory of Integrated Services Networks, Xidian University, Xian, 710071, China; Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, Dalian University of Technology, Dalian, 116024, China. Electronic address: rsliu@dlut.edu.cn.
authors.forename: Risheng
authors.initials: R
authors.lastname: Liu
authors.affiliation: School of Mathematical Sciences, Dalian University of Technology, Dalian, 116024, China. Electronic address: xdfighting@mail.dlut.edu.cn.
authors.forename: Di
authors.initials: D
authors.lastname: Wang
authors.affiliation: School of Mathematical Sciences, Dalian University of Technology, Dalian, 116024, China. Electronic address: yzhhan5@gmail.com.
authors.forename: Yuzhuo
authors.initials: Y
authors.lastname: Han
authors.affiliation: School of Software Technology, Dalian University of Technology, Dalian, 116024, China; Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, Dalian University of Technology, Dalian, 116024, China. Electronic address: xin.fan@ieee.org.
authors.forename: Xin
authors.initials: X
authors.lastname: Fan
authors.affiliation: School of Software Technology, Dalian University of Technology, Dalian, 116024, China; School of Mathematical Sciences, Dalian University of Technology, Dalian, 116024, China; Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, Dalian University of Technology, Dalian, 116024, China. Electronic address: zxluo@dlut.edu.cn.
authors.forename: Zhongxuan
authors.initials: Z
authors.lastname: Luo
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D006801:Humans; D008962:Models, Theoretical; D010363:Pattern Recognition, Automated / Q000379:methods*
keywords: Adaptive penalization; Low-rank subspace learning; Online optimization; Visual tracking