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 the 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 state-of-the-art methods.
Bibliographical noteFunding Information:
Manuscript received September 14, 2016; revised April 3, 2017 and December 21, 2017; accepted January 9, 2018. Date of publication January 24, 2018; date of current version May 1, 2018. This work was supported in part by the National Natural Science Foundation of China under Grant 61620106009, Grant 61332016, Grant U1636214, Grant 61650202, Grant 61572465, Grant 61390510, Grant 61732007, and Grant 61672188, in part by the Key Research Program of Frontier Sciences, CAS under Grant QYZDJ-SSW-SYS013, in part by NSF CAREER under Grant 1149783, and gifts from Adobe, Verisk, and Nvidia. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Christopher Wyatt. (Corresponding author: Qingming Huang.) Y. Qi is with the School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China (e-mail: email@example.com).
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All Science Journal Classification (ASJC) codes
- Computer Graphics and Computer-Aided Design