标题:Multiple-Shot Person Re-identification by Features Learned from Third-party Image Sets
作者:Zhao, Yanna; Wang, Lei; Zhao, Xu; Liu, Yuncai
作者机构:[Zhao, Yanna; Wang, Lei] Shandong Univ, Sch Informat Sci & Engn, Jinan 250100, Peoples R China.; [Zhao, Yanna; Wang, Lei; Zhao, Xu; Liu, Yuncai] Sha 更多
通讯作者:Zhao, Xu
通讯作者地址:[Zhao, X]Shanghai Jiao Tong Univ, Sch Automat, Shanghai 200240, Peoples R China.
来源:KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS
出版年:2015
卷:9
期:2
页码:775-792
DOI:10.3837/tiis.2015.02.017
关键词:Person re-identification; Appearance modeling; Max-margin feature; learning; Covariance matrix
摘要:Person re-identification is an important and challenging task in computer vision with numerous real world applications. Despite significant progress has been made in the past few years, person re-identification remains an unsolved problem. This paper presents a novel appearance-based approach to person re-identification. The approach exploits region covariance matrix and color histograms to capture the statistical properties and chromatic information of each object. Robustness against low resolution, viewpoint changes and pose variations is achieved by a novel signature, that is, the combination of Log Covariance Matrix feature and HSV histogram (LCMH). In order to further improve re-identification performance, third-party image sets are utilized as a common reference to sufficiently represent any image set with the same type. Distinctive and reliable features for a given image set are extracted through decision boundary between the specific set and a third-party image set supervised by max-margin criteria. This method enables the usage of an existing dataset to represent new image data without time-consuming data collection and annotation. Comparisons with state-of-the-art methods carried out on benchmark datasets demonstrate promising performance of our method.
收录类别:EI;SCOPUS;SCIE
资源类型:期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84924146262&doi=10.3837%2ftiis.2015.02.017&partnerID=40&md5=92fc997807e9c27cad404fc5e8956134
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