Partial Person Re-identification

被引：244

作者：

Zheng, Wei-Shi ^{[1
]}

Li, Xiang ^{[1
]}

Xiang, Tao ^{[2
]}

Liao, Shengcai ^{[3
]}

Lai, Jianhuang ^{[1
]}

Gong, Shaogang ^{[2
]}

机构：

[1] Sun Yat Sen Univ, Sch Informat Sci & Technol, Guangzhou, Guangdong, Peoples R China

[2] Queen Mary Univ London, Sch Elect Engn & Comp Sci, London, England

[3] Chinese Acad Sci, Inst Automat, NLPR, Beijing 100864, Peoples R China

来源：

2015 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV) | 2015年

关键词：

D O I：

10.1109/ICCV.2015.531

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We address a new partial person re-identification (reid) problem, where only a partial observation of a person is available for matching across different non-overlapping camera views. This differs significantly from the conventional person re-id setting where it is assumed that the full body of a person is detected and aligned. To solve this more challenging and realistic re-id problem without the implicit assumption of manual body-parts alignment, we propose a matching framework consisting of 1) a local patch-level matching model based on a novel sparse representation classification formulation with explicit patch ambiguity modelling, and 2) a global part-based matching model providing complementary spatial layout information. Our framework is evaluated on a new partial person re-id dataset as well as two existing datasets modified to include partial person images. The results show that the proposed method outperforms significantly existing re-id methods as well as other partial visual matching methods.

引用

页码：4678 / 4686

页数：9