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Person Re-identification by Attribute-Assisted Clothes Appearance

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Person Re-Identification

Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

Abstract

Person re-identification across nonoverlapping camera views is a challenging computer vision task. Due to the often low video quality and high camera position, it is difficult to get clear human faces. Therefore, clothes appearance is the main cue to re-identify a person. It is difficult to represent clothes appearance using low-level features due to its nonrigidity, but daily clothes have many characteristics in common. Based on this observation, we study person re-identification by embedding middle-level clothes attributes into the classifier via a latent support vector machine framework. We also collect a large-scale person re-identification dataset, and the effectiveness of the proposed method is demonstrated on this dataset under open-set experimental settings.

These authors Annan Li and Luoqi Liu contributed equally to this work.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering at National University of Singapore, Singapore, Singapore

    Annan Li, Luoqi Liu & Shuicheng Yan

Authors
  1. Annan Li
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  2. Luoqi Liu
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  3. Shuicheng Yan
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Corresponding author

Correspondence to Annan Li .

Editor information

Editors and Affiliations

  1. Queen Mary University, London, United Kingdom

    Shaogang Gong

  2. University of Verona, Italy

    Marco Cristani

  3. National University of Singapore, Singapore

    Shuicheng Yan

  4. The Chinese University of Hong Kong, Shatin, Hong Kong SAR

    Chen Change Loy

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© 2014 Springer-Verlag London

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Li, A., Liu, L., Yan, S. (2014). Person Re-identification by Attribute-Assisted Clothes Appearance. In: Gong, S., Cristani, M., Yan, S., Loy, C. (eds) Person Re-Identification. Advances in Computer Vision and Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-4471-6296-4_6

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  • DOI: https://doi.org/10.1007/978-1-4471-6296-4_6

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  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-6295-7

  • Online ISBN: 978-1-4471-6296-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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