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PA-Net: Learning local features using by pose attention for short-term person re-identification

文献类型：期刊论文

作者Wang, Kai3,4; Dong, Shichao4; Liu, Nian4; Yang, Junhui4; Li, Tao2,4; Hu, Qinghua1
刊名INFORMATION SCIENCES
出版日期2021-07-01
卷号565页码:196-209
关键词Person re-identification Pose attention Feature fusion Deep learning
ISSN号0020-0255
DOI10.1016/j.ins.2021.02.066
英文摘要Person re-identification (Re-ID) is an important but challenging task in video surveillance applications. In Re-ID tasks, pose is an extremely useful cue to identify a person, even from the back view. Therefore, pose-detection models may learn the features that are beneficial to the Re-ID task and improve the Re-ID performance by fusing the feature maps into the Re-ID model. Two key problems in integrating the pose cues are addressed in this study. One is how to reduce the noise caused by cross-domain datasets. The other is how to fuse the feature maps to better utilize high-level semantic pose cues. To address these two key problems, we first propose PA-Net by combining the pose attention stream and the global attention stream, where the global attention stream distinguishes persons with different global appearances, and the pose attention stream distinguishes persons with similar global appearance but different poses. Then, we present a pose attention stream that learns local features to reduce the noise in the pose cues caused by the cross-domain datasets and provide more semantic information for the Re-ID task. The effects of the proposed pose attention are demonstrated in an ablation study, and comparative experiments show that PA-Net achieves state-of-the-art performance. (c) 2021 Elsevier Inc. All rights reserved.
资助项目National Key Research and Development Program of China[2018YFB2100304] ; National Natural Science Foundation of China[61872200] ; National Natural Science Foundation of China[62002175] ; National Natural Science Foundation of China[61925602] ; National Natural Science Foundation of China[61732011] ; Open Project Fund of State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences[CARCH201905] ; Natural Science Foundation of Tianjin[19JCZDJC31600] ; Natural Science Foundation of Tianjin[18YFYZCG00060] ; CERNET Innovation Project[NGII20190402]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000653661400012
出版者ELSEVIER SCIENCE INC
源URL[http://119.78.100.204/handle/2XEOYT63/17556]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Li, Tao
作者单位1.Tianjin Univ, Sch Artificial Intelligence, Tianjin, Peoples R China
2.Chinese Acad Sci, Inst Comp Technol, State Key Lab Comp Architecture, Beijing, Peoples R China
3.Key Lab Med Data Anal & Stat Res Tianjin, Tianjin, Peoples R China
4.Nankai Univ, Coll Comp Sci, Tianjin, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Kai,Dong, Shichao,Liu, Nian,et al. PA-Net: Learning local features using by pose attention for short-term person re-identification[J]. INFORMATION SCIENCES,2021,565:196-209.
APA Wang, Kai,Dong, Shichao,Liu, Nian,Yang, Junhui,Li, Tao,&Hu, Qinghua.(2021).PA-Net: Learning local features using by pose attention for short-term person re-identification.INFORMATION SCIENCES,565,196-209.
MLA Wang, Kai,et al."PA-Net: Learning local features using by pose attention for short-term person re-identification".INFORMATION SCIENCES 565(2021):196-209.

入库方式： OAI收割

来源：计算技术研究所

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