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Re-identification by Covariance Descriptors

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Person Re-Identification

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

Abstract

This chapter addresses the problem of appearance matching, while employing the covariance descriptor. We tackle the extremely challenging case in which the same nonrigid object has to be matched across disjoint camera views. Covariance statistics averaged over a Riemannian manifold are fundamental for designing appearance models invariant to camera changes. We discuss different ways of extracting an object appearance by incorporating various training strategies. Appearance matching is enhanced either by discriminative analysis using images from a single camera or by selecting distinctive features in a covariance metric space employing data from two cameras. By selecting only essential features for a specific class of objects (e.g., humans) without defining a priori feature vector for extracting covariance, we remove redundancy from the covariance descriptor and ensure low computational cost. Using a feature selection technique instead of learning on a manifold, we avoid the over-fitting problem. The proposed models have been successfully applied to the person re-identification task in which a human appearance has to be matched across nonoverlapping cameras. We carry out detailed experiments of the suggested strategies, demonstrating their pros and cons w.r.t. recognition rate and suitability to video analytics systems.

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Notes

  1. 1.

    The Image Library for Intelligent Detection Systems (i-LIDS) is the UK government’s benchmark for Video Analytics (VA) systems.

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Acknowledgments

This work has been supported by VANAHEIM, ViCoMo and PANORAMA European projects.

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Authors and Affiliations

  1. INRIA, Sophia Antipolis, France

    Sławomir Bąk & François Brémond

Authors
  1. Sławomir Bąk
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  2. François Brémond
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Corresponding author

Correspondence to Sławomir Bąk .

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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Bąk, S., Brémond, F. (2014). Re-identification by Covariance Descriptors. 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_4

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  • DOI: https://doi.org/10.1007/978-1-4471-6296-4_4

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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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