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Discriminative Image Descriptors for Person Re-identification

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Person Re-Identification

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

Abstract

This chapter looks at person re-identification from a computer vision point of view, by proposing two new image descriptors designed for matching people bounding boxes in images. Indeed, one key issue of person re-identification is the ability to measure the similarity between two person-centered image regions, allowing to predict if these regions represent the same person despite changes in illumination, viewpoint, background clutter, occlusion, and image quality/resolution. They hence heavily rely on the signatures or descriptors used for representing and comparing the regions. The first proposed descriptor is a combination of Biologically Inspired Features (BIF) and covariance descriptors, while the second builds on the recent advances of Fisher Vectors. These two image descriptors are validated through experiments on two different person re-identification benchmarks (VIPeR and ETHZ), achieving state-of-the-art performance on both datasets.

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Notes

  1. 1.

    Remember that eBiCov is the combination of BiCov, MSCR, and wHSV.

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Acknowledgments

This work was partly realized as part of the Quaero Program funded by OSEO, French State agency for innovation and by the ANR, grant reference ANR-08-SECU-008-01/SCARFACE. The first author is partially supported by National Natural Science Foundation of China under contract No. 61003103.

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Authors and Affiliations

  1. School of Computer and Control Engineering, University of Chinese Academy of Sciences, Beijing, China

    Bingpeng Ma

  2. GREYC—CNRS UMR 6072, University of Caen Basse-Normandie, Caen, France

    Yu Su & Frédéric Jurie

Authors
  1. Bingpeng Ma
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  2. Yu Su
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  3. Frédéric Jurie
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Correspondence to Bingpeng Ma .

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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Ma, B., Su, Y., Jurie, F. (2014). Discriminative Image Descriptors for Person Re-identification. 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_2

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  • DOI: https://doi.org/10.1007/978-1-4471-6296-4_2

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  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-6295-7

  • Online ISBN: 978-1-4471-6296-4

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