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Person Re-identification: System Design and Evaluation Overview

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Person Re-Identification

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

Abstract

Person re-identification has important applications in video surveillance. It is particularly challenging because observed pedestrians undergo significant variations across camera views, and there are a large number of pedestrians to be distinguished given small pedestrian images from surveillance videos. This chapter discusses different approaches of improving the key components of a person re-identification system, including feature design, feature learning, and metric learning, as well as their strength and weakness. It provides an overview of various person re-identification systems and their evaluation on benchmark datasets. Multiple benchmark datasets for person re-identification are summarized and discussed. The performance of some state-of-the-art person identification approaches on benchmark datasets is compared and analyzed. It also discusses a few future research directions on improving benchmark datasets, evaluation methodology, and system design.

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Authors and Affiliations

  1. Department of Electronic Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong

    Xiaogang Wang & Rui Zhao

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  1. Xiaogang Wang
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  2. Rui Zhao
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Correspondence to Xiaogang Wang .

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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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Wang, X., Zhao, R. (2014). Person Re-identification: System Design and Evaluation Overview. 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_17

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  • DOI: https://doi.org/10.1007/978-1-4471-6296-4_17

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  • Print ISBN: 978-1-4471-6295-7

  • Online ISBN: 978-1-4471-6296-4

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