Person Re-identification by Salience Matching

被引：337

作者：

Zhao, Rui ^{[1
]}

Ouyang, Wanli ^{[1
]}

Wang, Xiaogang ^{[1
]}

机构：

[1] Chinese Univ Hong Kong, Dept Elect Engn, Hong Kong, Hong Kong, Peoples R China

来源：

2013 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV) | 2013年

关键词：

D O I：

10.1109/ICCV.2013.314

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Human salience is distinctive and reliable information in matching pedestrians across disjoint camera views. In this paper, we exploit the pairwise salience distribution relationship between pedestrian images, and solve the person re-identification problem by proposing a salience matching strategy. To handle the misalignment problem in pedestrian images, patch matching is adopted and patch salience is estimated. Matching patches with inconsistent salience brings penalty. Images of the same person are recognized by minimizing the salience matching cost. Furthermore, our salience matching is tightly integrated with patch matching in a unified structural RankSVM learning framework. The effectiveness of our approach is validated on the VIPeR dataset and the CUHK Campus dataset. It outperforms the state-of-the-art methods on both datasets.

引用

页码：2528 / 2535

页数：8