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题名:
Person Reidentification Based on Elastic Projections
作者: Li, Xuelong; Liu, Lina; Lu, Xiaoqiang
作者部门: 光学影像学习与分析中心
刊名: IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
出版日期: 2018-04-01
卷号: 29, 期号:4, 页码:1314-1327
关键词: Machine learning ; person reidentification ; representative and discriminative ; video surveillance
学科分类: Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
DOI: 10.1109/TNNLS.2016.2602855
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.
文章类型: Article
英文摘要: 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.
WOS标题词: Science & Technology ; Technology
类目[WOS]: Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
研究领域[WOS]: Computer Science ; Engineering
关键词[WOS]: RECOGNITION ; CLASSIFICATION ; FEATURES ; TRACKING ; RANKING
收录类别: SCI
语种: 英语
WOS记录号: WOS:000427859600044
ISSN号: 2162-237X
产权排序: 1
Citation statistics:
内容类型: 期刊论文
URI标识: http://ir.opt.ac.cn/handle/181661/30017
Appears in Collections:光学影像学习与分析中心_期刊论文

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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

Recommended Citation:
Li, Xuelong,Liu, Lina,Lu, Xiaoqiang. Person Reidentification Based on Elastic Projections[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2018-04-01,29(4):1314-1327.
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文件名: Person Reidentification Based on Elastic Projections.pdf
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