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The Re-identification Challenge

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Person Re-Identification

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

Abstract

For making sense of the vast quantity of visual data generated by the rapid expansion of large-scale distributed multi-camera systems, automated person re-identification is essential. However, it poses a significant challenge to computer vision systems. Fundamentally, person re-identification requires to solve two difficult problems of ‘finding needles in haystacks’ and ‘connecting the dots’ by identifying instances and associating the whereabouts of targeted people travelling across large distributed space–time locations in often crowded environments. This capability would enable the discovery of, and reasoning about, individual-specific long-term structured activities and behaviours. Whilst solving the person re-identification problem is inherently challenging, it also promises enormous potential for a wide range of practical applications, ranging from security and surveillance to retail and health care. As a result, the field has drawn growing and wide interest from academic researchers and industrial developers. This chapter introduces the re-identification problem, highlights the difficulties in building person re-identification systems, and presents an overview of recent progress and the state-of-the-art approaches to solving some of the fundamental challenges in person re-identification, benefiting from research in computer vision, pattern recognition and machine learning, and drawing insights from video analytics system design considerations for engineering practical solutions. It also provides an introduction of the contributing chapters of this book. The chapter ends by posing some open questions for the re-identification challenge arising from emerging and future applications.

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Author information

Authors and Affiliations

  1. Queen Mary University of London, London, UK

    Shaogang Gong & Timothy M. Hospedales

  2. University of Verona and Istituto Italiano di Tecnologia, Verona, Italy

    Marco Cristani

  3. The Chinese University of Hong Kong, Shatin, Hong Kong

    Chen Change Loy

Authors
  1. Shaogang Gong
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  2. Marco Cristani
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  3. Chen Change Loy
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  4. Timothy M. Hospedales
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Corresponding author

Correspondence to Shaogang Gong .

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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Gong, S., Cristani, M., Loy, C.C., Hospedales, T.M. (2014). The Re-identification Challenge. 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_1

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  • DOI: https://doi.org/10.1007/978-1-4471-6296-4_1

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  • Online ISBN: 978-1-4471-6296-4

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