[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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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]
Efficient tracker based on sparse coding with Euclidean local structure-based constraint
《智能系统学报》编辑部[ISSN 1673-4785/CN 23-1538/TP] 卷:
11
期数:
2016年第1期
页码:
136-147
栏目:
学术论文—知识工程
出版日期:
2016-02-25
- Title:
- Efficient tracker based on sparse coding with Euclidean local structure-based constraint
- 分类号:
- TP18;TP301.6
- 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
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