[1]WANG Hongyuan,ZHANG Ji,CHEN Fuhua.Efficient tracker based on sparse coding with Euclidean local structure-based constraint[J].智能系统学报编辑部,2016,11(1):136-147.[doi:10.11992/tis.201507073]
 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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《智能系统学报》编辑部[ISSN:1673-4785/CN:23-1538/TP]

卷:
第11卷
期数:
2016年1期
页码:
136-147
栏目:
出版日期:
2016-02-25

文章信息/Info

Title:
Efficient tracker based on sparse coding with Euclidean local structure-based constraint
作者:
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
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
关键词:
euclidean local-structure constraintl1-trackersparse codingtarget tracking
Keywords:
euclidean local-structure constraintl1-trackersparse codingtarget tracking
分类号:
TP18;TP301.6
DOI:
10.11992/tis.201507073
摘要:
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.
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.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2015-07-31;改回日期:。
基金项目:National Natural Foundation of China under Grant (61572085,61502058).
通讯作者:Hongyuan Wang.E-mail:hywang@cczu.edu.cn.
更新日期/Last Update: 1900-01-01