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Person Reidentification Based on Elastic Projections
Li, Xuelong; Liu, Lina; Lu, Xiaoqiang; Li, XL (reprint author), Chinese Acad Sci, Inst Opt & Precis Mech, Ctr OPTical IMagery Anal & Learning OPTIMAL, State Key Lab Transient Opt & Photon, Xian 710119, Shaanxi, Peoples R China.
Department光学影像学习与分析中心
2018-04-01
Source PublicationIEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
ISSN2162-237X
Volume29Issue:4Pages:1314-1327
Contribution Rank1
Abstract

Person reidentification usually refers to matching people in different camera views in nonoverlapping multicamera networks. Many existing methods learn a similarity measure by projecting the raw feature to a latent subspace to make the same target's distance smaller than different targets' distances. However, the same targets captured in different camera views should hold the same intrinsic attributes while different targets should hold different intrinsic attributes. Projecting all the data to the same subspace would cause loss of such an information and comparably poor discriminability. To address this problem, in this paper, a method based on elastic projections is proposed to learn a pairwise similarity measure for person reidentification. The proposed model learns two projections, positive projection and negative projection, which are both representative and discriminative. The representability refers to: for the same targets captured in two camera views, the positive projection can bridge the corresponding appearance variation and represent the intrinsic attributes of the same targets, while for the different targets captured in two camera views, the negative projection can explore and utilize the different attributes of different targets. The discriminability means that the intraclass distance should become smaller than its original distance after projection, while the interclass distance becomes larger on the contrary, which is the elastic property of the proposed model. In this case, prior information of the original data space is used to give guidance for the learning phase; more importantly, similar targets (but not the same) are effectively reduced by forcing the same targets to become more similar and different targets to become more distinct. The proposed model is evaluated on three benchmark data sets, including VIPeR, GRID, and CUHK, and achieves better performance than other methods.

SubtypeArticle
KeywordMachine Learning Person Reidentification Representative And Discriminative Video Surveillance
Subject AreaComputer Science, Artificial Intelligence
WOS HeadingsScience & Technology ; Technology
DOI10.1109/TNNLS.2016.2602855
Indexed BySCI ; EI
WOS KeywordRECOGNITION ; CLASSIFICATION ; FEATURES ; TRACKING ; RANKING
Language英语
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS IDWOS:000427859600044
EI Accession Number20171703591766
Citation statistics
Document Type期刊论文
Identifierhttp://ir.opt.ac.cn/handle/181661/30017
Collection光学影像学习与分析中心
Corresponding AuthorLi, XL (reprint author), Chinese Acad Sci, Inst Opt & Precis Mech, Ctr OPTical IMagery Anal & Learning OPTIMAL, State Key Lab Transient Opt & Photon, Xian 710119, Shaanxi, Peoples R China.
AffiliationChinese Acad Sci, Inst Opt & Precis Mech, Ctr OPTical IMagery Anal & Learning OPTIMAL, State Key Lab Transient Opt & Photon, Xian 710119, Shaanxi, Peoples R China
Recommended Citation
GB/T 7714
Li, Xuelong,Liu, Lina,Lu, Xiaoqiang,et al. Person Reidentification Based on Elastic Projections[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2018,29(4):1314-1327.
APA Li, Xuelong,Liu, Lina,Lu, Xiaoqiang,&Li, XL .(2018).Person Reidentification Based on Elastic Projections.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,29(4),1314-1327.
MLA Li, Xuelong,et al."Person Reidentification Based on Elastic Projections".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 29.4(2018):1314-1327.
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