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    Structure-aware Local Sparse Coding for Visual Tracking

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    08268563.pdf
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    Type
    Article
    Authors
    Qi, Yuankai
    Qin, Lei
    Zhang, Jian
    Zhang, Shengping
    Huang, Qingming
    Yang, Ming-Hsuan
    KAUST Department
    Visual Computing Center (VCC)
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2018-01-24
    Online Publication Date
    2018-01-24
    Print Publication Date
    2018-08
    Permanent link to this record
    http://hdl.handle.net/10754/627018
    
    Metadata
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    Abstract
    Sparse coding has been applied to visual tracking and related vision problems with demonstrated success in recent years. Existing tracking methods based on local sparse coding sample patches from a target candidate and sparsely encode these using a dictionary consisting of patches sampled from target template images. The discriminative strength of existing methods based on local sparse coding is limited as spatial structure constraints among the template patches are not exploited. To address this problem, we propose a structure-aware local sparse coding algorithm which encodes a target candidate using templates with both global and local sparsity constraints. For robust tracking, we show local regions of a candidate region should be encoded only with the corresponding local regions of the target templates that are the most similar from the global view. Thus, a more precise and discriminative sparse representation is obtained to account for appearance changes. To alleviate the issues with tracking drifts, we design an effective template update scheme. Extensive experiments on challenging image sequences demonstrate the effectiveness of the proposed algorithm against numerous stateof- the-art methods.
    Citation
    Qi Y, Qin L, Zhang J, Zhang S, Huang Q, et al. (2018) Structure-aware Local Sparse Coding for Visual Tracking. IEEE Transactions on Image Processing: 1–1. Available: http://dx.doi.org/10.1109/tip.2018.2797482.
    Sponsors
    This work was supported in part by National Natural Science Foundation of China: 61620106009, 61332016, U1636214, 61650202, 61572465, 61390510, 61732007, 61672188; in part by Key Research Program of Frontier Sciences, CAS: QYZDJ-SSWSYS013; in part by the NSF CAREER Grant 1149783, and gifts from Adobe, Verisk, and Nvidia.
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    IEEE Transactions on Image Processing
    DOI
    10.1109/tip.2018.2797482
    Additional Links
    http://ieeexplore.ieee.org/document/8268563/
    ae974a485f413a2113503eed53cd6c53
    10.1109/tip.2018.2797482
    Scopus Count
    Collections
    Articles; Visual Computing Center (VCC); Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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