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Body Symmetry and Part-Locality-Guided Direct Nonparametric Deep Feature Enhancement for Person Reidentification

文献类型：期刊论文

作者Zhu, Jianqing1,2; Zeng, Huanqiang3; Huang, Jingchang4; Zhu, Xiaobin5; Lei, Zhen6,7; Cai, Canhui1,2; Zheng, Lixin1,2
刊名IEEE INTERNET OF THINGS JOURNAL
出版日期2020-03-01
卷号7期号:3页码:2053-2065
关键词Body symmetry convolution neural network direct nonparametric deep feature enhancement (DNDFE) module part locality person reidentification (Re-ID)
ISSN号2327-4662
DOI10.1109/JIOT.2019.2960549
通讯作者Zeng, Huanqiang(zeng0043@hqu.edu.cn)
英文摘要In recent years, deep learning (DL) has been successfully and widely applied in the person reidentification (Re-ID). However, the DL-based person Re-ID methods face a bottleneck that the scales of most existing person Re-ID databases are not large enough for training very deep models. To address this problem, a body symmetry and part-locality-guided direct nonparametric deep feature enhancement (DNDFE) method is proposed in this article. Based on the observation that the body symmetry and part locality are two important appearance properties inherited in the upright walking persons, the proposed method designs two nonparametric layers, namely, the body symmetry average pooling and local normalization layers, to construct a DNDFE module to well explore the body symmetry and part locality properties. The proposed DNDFE module could be directly embedded between the traditional deep feature learning module and similarity learning module to enhance the DL features so as to improve the person Re-ID performance. The experimental results have shown that the proposed DNDFE method is superior to multiple state-of-the-art person Re-ID methods in terms of accuracy and efficiency.
资助项目National Natural Science Foundation of China[61976098] ; National Natural Science Foundation of China[61602191] ; National Natural Science Foundation of China[61871434] ; National Natural Science Foundation of China[61802136] ; National Natural Science Foundation of China[61876178] ; National Key Research and Development Program of China[2018YFB0803700] ; Natural Science Foundation of Fujian Province[2018J01090] ; Natural Science Foundation for Outstanding Young Scholars of Fujian Province[2019J06017] ; Open Foundation of Key Laboratory of Security Prevention Technology and Risk Assessment, People's Public Security University of China[18AFKF11] ; Key Science and Technology Project of Xiamen City[3502ZCQ20191005] ; Science and Technology Bureau of Quanzhou[2018C115R] ; Science and Technology Bureau of Quanzhou[2017G027] ; Science and Technology Bureau of Quanzhou[2017G036] ; Promotion Program for Young and Middle-Aged Teacher in Science and Technology Research of Huaqiao University[ZQN-PY418] ; Promotion Program for Young and Middle-Aged Teacher in Science and Technology Research of Huaqiao University[ZQN-YX403] ; Huaqiao University[16BS108] ; Huaqiao University[14BS201] ; Huaqiao University[14BS204]
WOS研究方向Computer Science ; Engineering ; Telecommunications
语种英语
WOS记录号WOS:000522265900040
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构National Natural Science Foundation of China ; National Key Research and Development Program of China ; Natural Science Foundation of Fujian Province ; Natural Science Foundation for Outstanding Young Scholars of Fujian Province ; Open Foundation of Key Laboratory of Security Prevention Technology and Risk Assessment, People's Public Security University of China ; Key Science and Technology Project of Xiamen City ; Science and Technology Bureau of Quanzhou ; Promotion Program for Young and Middle-Aged Teacher in Science and Technology Research of Huaqiao University ; Huaqiao University
源URL[http://ir.ia.ac.cn/handle/173211/38728]  
专题自动化研究所_模式识别国家重点实验室_生物识别与安全技术研究中心
通讯作者Zeng, Huanqiang
作者单位1.Huaqiao Univ, Coll Engn, Quanzhou 362021, Peoples R China
2.Huaqiao Univ, Fujian Prov Acad Engn Res Ctr Ind Intellectual Te, Quanzhou 362021, Peoples R China
3.Huaqiao Univ, Coll Informat Sci & Engn, Xiamen 361021, Peoples R China
4.Chinese Acad Sci, Shanghai Inst Microsyst & Informat Technol, Shanghai 201314, Peoples R China
5.Univ Sci & Technol Beijing, Sch Comp & Commun Engn, Beijing 100083, Peoples R China
6.Chinese Acad Sci, Inst Automat, Ctr Biometr & Secur Res, Beijing 100190, Peoples R China
7.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Zhu, Jianqing,Zeng, Huanqiang,Huang, Jingchang,et al. Body Symmetry and Part-Locality-Guided Direct Nonparametric Deep Feature Enhancement for Person Reidentification[J]. IEEE INTERNET OF THINGS JOURNAL,2020,7(3):2053-2065.
APA Zhu, Jianqing.,Zeng, Huanqiang.,Huang, Jingchang.,Zhu, Xiaobin.,Lei, Zhen.,...&Zheng, Lixin.(2020).Body Symmetry and Part-Locality-Guided Direct Nonparametric Deep Feature Enhancement for Person Reidentification.IEEE INTERNET OF THINGS JOURNAL,7(3),2053-2065.
MLA Zhu, Jianqing,et al."Body Symmetry and Part-Locality-Guided Direct Nonparametric Deep Feature Enhancement for Person Reidentification".IEEE INTERNET OF THINGS JOURNAL 7.3(2020):2053-2065.

入库方式： OAI收割

来源：自动化研究所

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