[1]WANG Hongyuan,ZHANG Ji,CHEN Fuhua.Efficient tracker based on sparse coding with Euclidean local structure-based constraint[J].CAAI Transactions on Intelligent Systems,2016,11(1):136-147.[doi:10.11992/tis.201507073]
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Efficient tracker based on sparse coding with Euclidean local structure-based constraint

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 11 Number of periods: 2016 1 Page number: 136-147 Column: 学术论文—知识工程 Public date: 2016-02-25
Title:
Efficient tracker based on sparse coding with Euclidean local structure-based constraint
Author(s):
WANG Hongyuan1;  ZHANG Ji1;  CHEN Fuhua2
1. School of Information Science and Engineering, Changzhou University, Changzhou, Jiangsu, China 213164;
2. Department of Nat-ural Science and Mathematics, West Liberty University, West Virginia, United States 26074
Keywords:
euclidean local-structure constraint; l1-tracker; sparse coding; target tracking
CLC:
TP18;TP301.6
DOI:
10.11992/tis.201507073
Abstract:
Sparse coding (SC) based visual tracking (l1-tracker) is gaining increasing attention, and many related algorithms are developed. In these algorithms, each candidate region is sparsely represented as a set of target tem-plates. However, the structure connecting these candidate regions is usually ignored. Lu proposed an NLSSC-tracker with non-local self-similarity sparse coding to address this issue, which has a high computational cost. In this study, we propose an Euclidean local-structure constraint based sparse coding tracker with a smoothed Euclidean local structure. With this tracker, the optimization procedure is transformed to a small-scale l1-optimization problem, sig-nificantly reducing the computational cost. Extensive experimental results on visual tracking demonstrate the effectiveness and efficiency of the proposed algorithm.
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