Segment attention-guided part-aligned network for person re-identification

被引：0

作者：

Wang, Wen ^{[1
,2
]}

Liu, Yongwen ^{[1
,2
]}

An, Gaoyun ^{[1
,2
]}

机构：

[1] Beijing Jiaotong Univ, Inst Informat Sci, Beijing, Peoples R China

[2] Beijing Key Lab Adv Informat Sci & Network Techno, Beijing, Peoples R China

来源：

ELECTRONICS LETTERS | 2021年 / 57卷 / 13期

基金：

中国国家自然科学基金;

关键词：

Computer vision and image processing techniques; Image recognition; Optical; image and video signal processing;

D O I：

10.1049/ell2.12178

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Part misalignment of the human body caused by complex variations in viewpoint and pose poses a fundamental challenge to person re-identification. This letter examines Res2Net as the backbone network to extract multi-scale appearance features. At the same time, it uses the human parsing model to extract part features, which can be used as an attention stream to guide part features re-calibration from the spatial dimension. Additionally, in order to ensure the diversity of features, SAG-PAN effectively integrates the global appearance features of person image with part fine-grained features. The experimental results on the Market-1501, DukeMTMC-reID and CUHK03 datasets show that the proposed SAG-PAN achieved superior performance against the existing state-of-the-art methods.

引用

页码：508 / 510

页数：3