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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.
作者部门光学影像学习与分析中心
2018-04-01
发表期刊IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
ISSN2162-237X
卷号29期号:4页码:1314-1327
产权排序1
摘要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.
文章类型Article
关键词Machine Learning Person Reidentification Representative And Discriminative Video Surveillance
学科领域Computer Science, Artificial Intelligence
WOS标题词Science & Technology ; Technology
DOI10.1109/TNNLS.2016.2602855
收录类别SCI
关键词[WOS]RECOGNITION ; CLASSIFICATION ; FEATURES ; TRACKING ; RANKING
语种英语
WOS研究方向Computer Science ; Engineering
WOS类目Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号WOS:000427859600044
引用统计
文献类型期刊论文
条目标识符http://ir.opt.ac.cn/handle/181661/30017
专题光学影像学习与分析中心
通讯作者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.
作者单位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
推荐引用方式
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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