title: Online multi-modal robust non-negative dictionary learning for visual tracking.
abstract: Dictionary learning is a method of acquiring a collection of atoms for subsequent signal representation. Due to its excellent representation ability, dictionary learning has been widely applied in multimedia and computer vision. However, conventional dictionary learning algorithms fail to deal with multi-modal datasets. In this paper, we propose an online multi-modal robust non-negative dictionary learning (OMRNDL) algorithm to overcome this deficiency. Notably, OMRNDL casts visual tracking as a dictionary learning problem under the particle filter framework and captures the intrinsic knowledge about the target from multiple visual modalities, e.g., pixel intensity and texture information. To this end, OMRNDL adaptively learns an individual dictionary, i.e., template, for each modality from available frames, and then represents new particles over all the learned dictionaries by minimizing the fitting loss of data based on M-estimation. The resultant representation coefficient can be viewed as the common semantic representation of particles across multiple modalities, and can be utilized to track the target. OMRNDL incrementally learns the dictionary and the coefficient of each particle by using multiplicative update rules to respectively guarantee their non-negativity constraints. Experimental results on a popular challenging video benchmark validate the effectiveness of OMRNDL for visual tracking in both quantity and quality.
authors.affiliation: Science and Technology on Parallel and Distributed Processing Laboratory, College of Computer, National University of Defense Technology, Changsha, Hunan, China.
authors.forename: Xiang
authors.initials: X
authors.lastname: Zhang
authors.affiliation: Science and Technology on Parallel and Distributed Processing Laboratory, College of Computer, National University of Defense Technology, Changsha, Hunan, China.
authors.forename: Naiyang
authors.initials: N
authors.lastname: Guan
authors.affiliation: The Centre for Quantum Computation & Intelligent Systems and the Faculty of Engineering and Information Technology, University of Technology, Sydney, 81 Broadway Street, Ultimo, NSW 2007, Australia.
authors.forename: Dacheng
authors.initials: D
authors.lastname: Tao
authors.affiliation: College of Information System and Management, National University of Defense Technology, Changsha, Hunan, 410073 China.
authors.forename: Xiaogang
authors.initials: X
authors.lastname: Qiu
authors.affiliation: Science and Technology on Parallel and Distributed Processing Laboratory, College of Computer, National University of Defense Technology, Changsha, Hunan, China.
authors.forename: Zhigang
authors.initials: Z
authors.lastname: Luo
mesh_terms: D000465:Algorithms; D004014:Dictionaries as Topic; D020407:Internet; D007858:Learning; D008962:Models, Theoretical