Structure-aware Local Sparse Coding for Visual Tracking

Yuankai Qi, Lei Qin, Jian Zhang, Shengping Zhang, Qingming Huang, Ming-Hsuan Yang

Research output: Contribution to journalArticlepeer-review

49 Scopus citations


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.
Original languageEnglish (US)
Pages (from-to)3857-3869
Number of pages13
JournalIEEE Transactions on Image Processing
Issue number8
StatePublished - Jan 24 2018

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: 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.


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