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    In Defense of Sparse Tracking: Circulant Sparse Tracker

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    Type
    Conference Paper
    Authors
    Zhang, Tianzhu
    Bibi, Adel cc
    Ghanem, Bernard cc
    KAUST Department
    Visual Computing Center (VCC)
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Electrical Engineering Program
    Date
    2016-12-13
    Online Publication Date
    2016-12-13
    Print Publication Date
    2016-06
    Permanent link to this record
    http://hdl.handle.net/10754/622775
    
    Metadata
    Show full item record
    Abstract
    Sparse 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.
    Citation
    Zhang 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.
    Sponsors
    Research in this publication was supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research.
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
    DOI
    10.1109/CVPR.2016.421
    Additional Links
    http://ieeexplore.ieee.org/document/7780790/
    ae974a485f413a2113503eed53cd6c53
    10.1109/CVPR.2016.421
    Scopus Count
    Collections
    Conference Papers; Electrical Engineering Program; Visual Computing Center (VCC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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