CASIA OpenIR  > 模式识别国家重点实验室  > 多媒体计算与图形学
Robust Visual Tracking via Structured Multi-Task Sparse Learning
Tianzhu Zhang1; Bernard Ghanem2; Si Liu3; Narendra Ahuja4
Source PublicationInternational Journal of Computer Vision
2013
Volume101Issue:2Pages:367-383
Subtype期刊
AbstractIn this paper, we formulate object tracking in a particle filter framework as a structured multi-task sparse learning problem, which we denote as Structured Multi-Task Tracking (S-MTT). Since we model particles as linear combinations of dictionary templates that are updated dynamically, learning the representation of each particle is considered a single task in Multi-Task Tracking (MTT). By employing popular sparsity-inducing mixed norms and we regularize the representation problem to enforce joint sparsity and learn the particle representations together. As compared to previous methods that handle particles independently, our results demonstrate that mining the interdependencies between particles improves tracking performance and overall computational complexity. Interestingly, we show that the popular tracker (Mei and Ling, IEEE Trans Pattern Anal Mach Intel 33(11):2259-2272, 2011) is a special case of our MTT formulation (denoted as the tracker) when Under the MTT framework, some of the tasks (particle representations) are often more closely related and more likely to share common relevant covariates than other tasks. Therefore, we extend the MTT framework to take into account pairwise structural correlations between particles (e.g. spatial smoothness of representation) and denote the novel framework as S-MTT. The problem of learning the regularized sparse representation in MTT and S-MTT can be solved efficiently using an Accelerated Proximal Gradient (APG) method that yields a sequence of closed form updates. As such, S-MTT and MTT are computationally attractive. We test our proposed approach on challenging sequences involving heavy occlusion, drastic illumination changes, and large pose variations. Experimental results show that S-MTT is much better than MTT, and both methods consistently outperform state-of-the-art trackers.
KeywordVisual Tracking Particle Filter Graph Structure Sparse Representation Multi-task Learning
WOS HeadingsScience & Technology ; Technology
Subject AreaComputer Science
DOI10.1007/s11263-012-0582-z
WOS KeywordOBJECT TRACKING ; PARTICLE FILTER ; EIGENTRACKING ; MODELS
URL查看原文
Indexed BySCI
Language英语
Funding ProjectAdvanced Digital Sciences Center from Singapore's Agency for Science, Technology and Research (A*STAR)
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000314291600008
Citation statistics
Cited Times:186[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/13643
Collection模式识别国家重点实验室_多媒体计算与图形学
Corresponding AuthorSi Liu
Affiliation1.Advanced Digital Sciences Center, Singapore
2.King Abdullah University of Science and Technology
3.National University of Singapore
4.University of Illinois at Urbana-Champaign
Recommended Citation
GB/T 7714
Tianzhu Zhang,Bernard Ghanem,Si Liu,et al. Robust Visual Tracking via Structured Multi-Task Sparse Learning[J]. International Journal of Computer Vision,2013,101(2):367-383.
APA Tianzhu Zhang,Bernard Ghanem,Si Liu,&Narendra Ahuja.(2013).Robust Visual Tracking via Structured Multi-Task Sparse Learning.International Journal of Computer Vision,101(2),367-383.
MLA Tianzhu Zhang,et al."Robust Visual Tracking via Structured Multi-Task Sparse Learning".International Journal of Computer Vision 101.2(2013):367-383.
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