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dc.contributor.authorZhang, Tianzhu
dc.contributor.authorBibi, Adel Aamer
dc.contributor.authorGhanem, Bernard
dc.date.accessioned2017-01-29T13:51:38Z
dc.date.available2017-01-29T13:51:38Z
dc.date.issued2016-12-13
dc.identifier.citationZhang T, Bibi A, Ghanem B (2016) In Defense of Sparse Tracking: Circulant Sparse Tracker. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Available: http://dx.doi.org/10.1109/CVPR.2016.421.
dc.identifier.doi10.1109/CVPR.2016.421
dc.identifier.urihttp://hdl.handle.net/10754/622775
dc.description.abstractSparse representation has been introduced to visual tracking by finding the best target candidate with minimal reconstruction error within the particle filter framework. However, most sparse representation based trackers have high computational cost, less than promising tracking performance, and limited feature representation. To deal with the above issues, we propose a novel circulant sparse tracker (CST), which exploits circulant target templates. Because of the circulant structure property, CST has the following advantages: (1) It can refine and reduce particles using circular shifts of target templates. (2) The optimization can be efficiently solved entirely in the Fourier domain. (3) High dimensional features can be embedded into CST to significantly improve tracking performance without sacrificing much computation time. Both qualitative and quantitative evaluations on challenging benchmark sequences demonstrate that CST performs better than all other sparse trackers and favorably against state-of-the-art methods.
dc.description.sponsorshipResearch in this publication was supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttp://ieeexplore.ieee.org/document/7780790/
dc.subjectobject tracking
dc.subjectoptimisation
dc.subjectparticle filtering (numerical methods)
dc.subjectComputational modeling
dc.subjectDictionaries
dc.subjectImage color analysis
dc.subjectRobustness
dc.subjectTarget tracking
dc.subjectVisualization
dc.titleIn Defense of Sparse Tracking: Circulant Sparse Tracker
dc.typeConference Paper
dc.contributor.departmentVisual Computing Center (VCC)
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentElectrical Engineering Program
dc.identifier.journal2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
dc.contributor.institutionInstitute of Automation, Chinese Academy of Sciences (CASIA), China
kaust.personZhang, Tianzhu
kaust.personBibi, Adel Aamer
kaust.personGhanem, Bernard


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