CASIA OpenIR  > 模式识别国家重点实验室  > 生物识别与安全技术
Object Reidentification via Joint Quadruple Decorrelation Directional Deep Networks in Smart Transportation
Zhu, Jianqing1; Huang, Jingchang2; Zeng, Huanqiang3; Ye, Xiaoqing2; Li, Baoqing2; Lei, Zhen4; Zheng, Lixin1
Source PublicationIEEE INTERNET OF THINGS JOURNAL
ISSN2327-4662
2020-04-01
Volume7Issue:4Pages:2944-2954
Corresponding AuthorZeng, Huanqiang(zeng0043@hqu.edu.cn)
AbstractObject reidentification with the goal of matching pedestrian or vehicle images captured from different camera viewpoints is of considerable significance to public security. Quadruple directional deep learning features (QD-DLFs) can comprehensively describe object images. However, the correlation among QD-DLFs is an unavoidable problem, since QD-DLFs are learned with quadruple independent directional deep networks (QIDDNs) driven with the same training data, and each network holds the same basic deep feature learning architecture (BDFLA). The correlation among QD-DLFs is harmful to the complementarity of QD-DLFs, restricting the object reidentification performance. For that, we propose joint quadruple decorrelation directional deep networks (JQD(3)Ns) to reduce the correlation among the learned QD-DLFs. In order to jointly train JQD(3)Ns, besides the softmax loss functions, a parameter correlation cost function is proposed to indirectly reduce the correlation among QD-DLFs by enlarging the dissimilarity among the parameters of JQD(3)Ns. Extensive experiments on three publicly available large-scale data sets demonstrate that the proposed JQD(3)Ns approach is superior to multiple state-of-the-art object reidentification methods.
KeywordDeep learning pedestrian reidentification smart transportation vehicle reidentification
DOI10.1109/JIOT.2020.2963996
WOS KeywordPERSON REIDENTIFICATION ; VEHICLE REIDENTIFICATION ; IOT ; FINGERPRINT ; INTERNET
Indexed BySCI
Language英语
Funding ProjectNational 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] ; 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] ; Scientific Research Funds of Huaqiao University[16BS108] ; Scientific Research Funds of Huaqiao University[14BS201] ; Scientific Research Funds of Huaqiao University[14BS204]
Funding OrganizationNational Natural Science Foundation 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 ; Scientific Research Funds of Huaqiao University
WOS Research AreaComputer Science ; Engineering ; Telecommunications
WOS SubjectComputer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS IDWOS:000537136400039
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Cited Times:6[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/39631
Collection模式识别国家重点实验室_生物识别与安全技术
Corresponding AuthorZeng, Huanqiang
Affiliation1.Huaqiao Univ, Coll Engn, Quanzhou 362021, Peoples R China
2.Chinese Acad Sci, Shanghai Inst Microsyst & Informat Technol, Shanghai 201314, Peoples R China
3.Huaqiao Univ, Coll Informat Sci & Engn, Xiamen 361021, Peoples R China
4.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Zhu, Jianqing,Huang, Jingchang,Zeng, Huanqiang,et al. Object Reidentification via Joint Quadruple Decorrelation Directional Deep Networks in Smart Transportation[J]. IEEE INTERNET OF THINGS JOURNAL,2020,7(4):2944-2954.
APA Zhu, Jianqing.,Huang, Jingchang.,Zeng, Huanqiang.,Ye, Xiaoqing.,Li, Baoqing.,...&Zheng, Lixin.(2020).Object Reidentification via Joint Quadruple Decorrelation Directional Deep Networks in Smart Transportation.IEEE INTERNET OF THINGS JOURNAL,7(4),2944-2954.
MLA Zhu, Jianqing,et al."Object Reidentification via Joint Quadruple Decorrelation Directional Deep Networks in Smart Transportation".IEEE INTERNET OF THINGS JOURNAL 7.4(2020):2944-2954.
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