Robust Visual Tracking Using Structurally Random Projection and Weighted Least Squares

CORC > 西安光学精密机械研究所 > 中国科学院西安光学精密机械研究所 > 光学影像学习与分析中心

	Robust Visual Tracking Using Structurally Random Projection and Weighted Least Squares
	Zhang, Shengping 1; Zhou, Huiyu 2; Jiang, Feng 3; Li, Xuelong4
刊名	ieee transactions on circuits and systems for video technology
	2015-11-01
卷号	25 期号:11 页码:1749-1760
关键词	Sparse representation structural random projection (RP) visual tracking weighted least squares (WLS)
ISSN号	10518215
产权排序	4
英文摘要	sparse representation-based visual tracking approaches have attracted increasing interests in the community in recent years. the main idea is to linearly represent each target candidate using a set of target and trivial templates, while imposing a sparsity constraint onto the representation coefficients. after we obtain the coefficients using l(1)-norm minimization methods, the candidate with the lowest error, when it is reconstructed using only the target templates and the associated coefficients, is considered as the tracking result. in spite of promising system performance widely reported, it is unclear if the performance of these trackers can be maximized. in addition, computational complexity caused by the dimensionality of the feature space limits these algorithms in real-time applications. in this paper, we propose a real-time visual tracking method based on structurally random projection (rp) and weighted least squares (wls) techniques. in particular, to enhance the discriminative capability of the tracker, we introduce background templates to the linear representation framework. to handle appearance variations over time, we relax the sparsity constraint using a wls method to obtain the representation coefficients. to further reduce the computational complexity, structurally rp is used to reduce the dimensionality of the feature space, while preserving the pairwise distances between the data points in the feature space. experimental results show that the proposed approach outperforms several state-of-the-art tracking methods.
学科主题	engineering, electrical & electronic
WOS标题词	science & technology ; technology
类目[WOS]	engineering, electrical & electronic
研究领域[WOS]	engineering
关键词[WOS]	sparse appearance model ; object tracking ; random matrices ; discriminant-analysis ; representation ; selection ; fusion ; features
收录类别	SCI ; EI
语种	英语
WOS记录号	WOS:000364230600003
内容类型	期刊论文
源URL	[http://ir.opt.ac.cn/handle/181661/27498]
专题	西安光学精密机械研究所_光学影像学习与分析中心
作者单位	1.Harbin Inst Technol Weihai, Sch Comp Sci & Technol, Weihai 264209, Peoples R China 2.Queens Univ Belfast, Inst Elect Commun & Informat Technol, Belfast BT3 9DT, Antrim, North Ireland 3.Harbin Inst Technol, Sch Comp Sci & Technol, Harbin 150001, Peoples R China 4.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Ctr Opt Imagery Anal & Learning, State Key Lab Transient Opt & Photon, Xian 710119, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Shengping,Zhou, Huiyu,Jiang, Feng,et al. Robust Visual Tracking Using Structurally Random Projection and Weighted Least Squares[J]. ieee transactions on circuits and systems for video technology,2015,25(11):1749-1760.
APA	Zhang, Shengping,Zhou, Huiyu,Jiang, Feng,&Li, Xuelong.(2015).Robust Visual Tracking Using Structurally Random Projection and Weighted Least Squares.ieee transactions on circuits and systems for video technology,25(11),1749-1760.
MLA	Zhang, Shengping,et al."Robust Visual Tracking Using Structurally Random Projection and Weighted Least Squares".ieee transactions on circuits and systems for video technology 25.11(2015):1749-1760.