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

doi:10.1109/JIOT.2019.2960549

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 生物识别与安全技术

	Body Symmetry and Part-Locality-Guided Direct Nonparametric Deep Feature Enhancement for Person Reidentification
	Zhu, Jianqing 1,2; Zeng, Huanqiang 3; Huang, Jingchang 4; Zhu, Xiaobin 5; Lei, Zhen6,7 ; Cai, Canhui 1,2; Zheng, Lixin 1,2
发表期刊	IEEE INTERNET OF THINGS JOURNAL
ISSN	2327-4662
	2020-03-01
卷号	7 期号:3 页码:2053-2065
通讯作者	Zeng, Huanqiang(zeng0043@hqu.edu.cn)
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.
关键词	Body symmetry convolution neural network direct nonparametric deep feature enhancement (DNDFE) module part locality person reidentification (Re-ID)
DOI	10.1109/JIOT.2019.2960549
收录类别	SCI
语种	英语
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]
项目资助者	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
WOS研究方向	Computer Science ; Engineering ; Telecommunications
WOS类目	Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS记录号	WOS:000522265900040
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：17[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/38729
专题	多模态人工智能系统全国重点实验室_生物识别与安全技术
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.