Bi-level Relative Information Analysis for Multiple-Shot Person Re-Identification

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概要

• 論文の詳細を見る

• Multiple-shot person re-identification, which is valuable for application in visual surveillance, tackles the problem of building the correspondence between images of the same person from different cameras. It is challenging because of the large within-class variations due to the changeable body appearance and environment and the small between-class differences arising from the possibly similar body shape and clothes style. A novel method named "Bi-level Relative Information Analysis" is proposed in this paper for the issue by treating it as a set-based ranking problem. It creatively designs a relative dissimilarity using set-level neighborhood information, called "Set-level Common-Near-Neighbor Modeling", complementary to the sample-level relative feature "Third-Party Collaborative Representation" which has recently been proven to be quite effective for multiple-shot person re-identification. Experiments implemented on several public benchmark datasets show significant improvements over state-of-the-art methods.

• The Institute of Electronics, Information and Communication Engineersの論文

著者

• MINOH Michihiko

  Academic Center for Computing and Media Studies, Kyoto University

• Minoh Michihiko

  Academic Center For Computing And Media Studies Kyoto University

• Li Wei

  Graduate School Of Engineering University Of Hyogo

• WU Yang

  Academic Center for Computing and Media Studies, Kyoto University

• MUKUNOKI Masayuki

  Academic Center for Computing and Media Studies, Kyoto University

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• Bi-level Relative Information Analysis for Multiple-Shot Person Re-Identification

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