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dc.contributor.authorZhang, Tianzhu
dc.contributor.authorYang, Ming-Hsuan
dc.contributor.authorAhuja, Narendra
dc.contributor.authorGhanem, Bernard
dc.contributor.authorYan, Shuicheng
dc.contributor.authorXu, Changsheng
dc.contributor.authorLiu, Si
dc.date.accessioned2015-06-01T15:06:39Z
dc.date.available2015-06-01T15:06:39Z
dc.date.issued2015-06-01
dc.identifier.urihttp://hdl.handle.net/10754/556107
dc.description.abstractSparse representation has been applied to visual tracking by finding the best target candidate with minimal reconstruction error by use of target templates. However, most sparse representation based trackers only consider holistic or local representations and do not make full use of the intrinsic structure among and inside target candidates, thereby making the representation less effective when similar objects appear or under occlusion. In this paper, we propose a novel Structural Sparse Tracking (SST) algorithm, which not only exploits the intrinsic relationship among target candidates and their local patches to learn their sparse representations jointly, but also preserves the spatial layout structure among the local patches inside each target candidate. We show that our SST algorithm accommodates most existing sparse trackers with the respective merits. Both qualitative and quantitative evaluations on challenging benchmark image sequences demonstrate that the proposed SST algorithm performs favorably against several state-of-the-art methods.
dc.description.sponsorshipIEEE Computer Society
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttp://ivul.kaust.edu.sa/Pages/Pub-Sparse-Tracking.aspx
dc.rights(c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
dc.subjectTracking
dc.subjectSparse Optimization
dc.titleStructural Sparse Tracking
dc.typeConference Paper
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journalProceedings of the IEEE Conference on Computer Vision and Pattern Recognition
dc.conference.date07 Jun - 12 Jun 2015
dc.conference.name2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
dc.conference.locationHynes Convention Center 900 Boylston St Boston, MA, USA
dc.eprint.versionPost-print
dc.contributor.institutionAdvanced Digital Sciences Center
dc.contributor.institutionInstitute of Automation, CAS
dc.contributor.institutionInstitute of Information Engineering, CAS
dc.contributor.institutionChina-Singapore Institute of Digital Media
dc.contributor.institutionNational University of Singapore
dc.contributor.institutionUniversity of Illinois at Urbana-Champaign
dc.contributor.institutionUniversity of California at Merced
refterms.dateFOA2018-06-14T07:57:34Z


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