Dense Invariant Feature Based Support Vector Ranking for Person Re-identification

被引:0
|
作者
Tan, Shoubiao [1 ]
Zheng, Feng [2 ]
Shao, Ling [3 ]
机构
[1] Anhui Univ, Minist Educ, Key Lab Intelligent Comp & Signal Proc, Hefei 230039, Peoples R China
[2] Univ Sheffield, Dept Elect & Elect Engn, Sheffield S1 3JD, S Yorkshire, England
[3] Northumbria Univ, Dept Comp Sci & Digital Technol, Newcastle Upon Tyne NE1 8ST, Tyne & Wear, England
来源
2015 IEEE GLOBAL CONFERENCE ON SIGNAL AND INFORMATION PROCESSING (GLOBALSIP) | 2015年
关键词
Person Re-identification; Dense Invariant Feature; Support Vector Ranking; Feature Fusion;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Recently, support vector ranking has been adopted to address the challenging person re-identification problem. However, the ranking model based on ordinary global features cannot represent the significant variation of pose and viewpoint across camera views. Thus, a novel ranking method which fuses the dense invariant features is proposed in this paper to model the variation of images across camera views. By maximizing the margin and minimizing the error score for the fused features, an optimal space for ranking has been learned. Due to the invariance of the dense invariant features and the fusion of the bidirectional features, the proposed method significantly outperforms the original support vector ranking algorithm and is competitive with state-of-the-art techniques on two challenging datasets, showing its potential for real-world person re-identification.
引用
收藏
页码:687 / 691
页数:5
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