Person Re-Identification

  • Shaogang Gong
  • Marco Cristani
  • Shuicheng Yan
  • Chen Change Loy

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

Table of contents

  1. Front Matter
    Pages i-xviii
  2. Shaogang Gong, Marco Cristani, Chen Change Loy, Timothy M. Hospedales
    Pages 1-20
  3. Features and Representations

    1. Front Matter
      Pages 21-21
    2. Bingpeng Ma, Yu Su, Frédéric Jurie
      Pages 23-42
    3. Loris Bazzani, Marco Cristani, Vittorio Murino
      Pages 43-69
    4. Sławomir Bąk, François Brémond
      Pages 71-91
    5. Ryan Layne, Timothy M. Hospedales, Shaogang Gong
      Pages 93-117
    6. Annan Li, Luoqi Liu, Shuicheng Yan
      Pages 119-138
    7. Dong Seon Cheng, Marco Cristani
      Pages 139-160
    8. Matteo Munaro, Andrea Fossati, Alberto Basso, Emanuele Menegatti, Luc Van Gool
      Pages 161-181
    9. Wei-Shi Zheng, Shaogang Gong, Tao Xiang
      Pages 183-201
    10. Chunxiao Liu, Shaogang Gong, Chen Change Loy, Xinggang Lin
      Pages 203-228
  4. Matching and Distance Metric

    1. Front Matter
      Pages 229-229
    2. Tamar Avraham, Michael Lindenbaum
      Pages 231-246
    3. Peter M. Roth, Martin Hirzer, Martin Köstinger, Csaba Beleznai, Horst Bischof
      Pages 247-267
    4. Svebor Karaman, Giuseppe Lisanti, Andrew D. Bagdanov, Alberto Del Bimbo
      Pages 287-307
    5. François Fleuret, Horesh Ben Shitrit, Pascal Fua
      Pages 309-330
  5. Evaluation and Application

    1. Front Matter
      Pages 331-331
    2. Roberto Vezzani, Rita Cucchiara
      Pages 333-349

About this book


Re-identification offers a useful tool for non-invasive biometric validation, surveillance, and human-robot interaction in a broad range of applications from crowd traffic management to personalised healthcare.

This comprehensive volume is the first work of its kind dedicated to addressing the challenge of Person Re-Identification, presenting insights from an international selection of leading authorities in the field. Taking a strongly multidisciplinary approach, the text provides an in-depth discussion of recent developments and state-of-the-art methods drawn from the computer vision, pattern recognition and machine learning communities, embracing both fundamental research and practical applications.

Topics and features:

  • Introduces examples of robust feature representations, reviews salient feature weighting and selection mechanisms, and examines the benefits of semantic attributes
  • Describes how to segregate meaningful body parts from background clutter
  • Examines the use of 3D depth images, and contextual constraints derived from the visual appearance of a group
  • Reviews approaches to feature transfer function and distance metric learning, and discusses potential solutions to issues of data scalability and identity inference
  • Investigates the limitations of existing benchmark datasets, presents strategies for camera topology inference, and describes techniques for improving post-rank search efficiency
  • Explores the design rationale and implementation considerations of building a practical re-identification system

This timely collection will be of great interest to academics, industrial researchers and postgraduates involved in computer vision and machine learning, database image retrieval, big data mining, and search engines, as well as to developers keen to exploit this emerging technology for commercial applications.


3D Computer Vision Active Learning Attribute Learning Behavioral Biometrics Big Data Search Context-Aware Recognition Data Fusion Data Mining Distributed Camera Networks Human Recognition Image Retrieval Multi-Camera Tracking Multi-Instance Learning People Detection Person Re-Identification Soft Biometrics Transfer Learning Video Content Analysis Visual Surveillance

Editors and affiliations

  • Shaogang Gong
    • 1
  • Marco Cristani
    • 2
  • Shuicheng Yan
    • 3
  • Chen Change Loy
    • 4
  1. 1.Queen Mary UniversityLondonUnited Kingdom
  2. 2.University of VeronaItaly
  3. 3.National University of SingaporeSingapore
  4. 4.The Chinese University of Hong KongShatinHong Kong SAR

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag London 2014
  • Publisher Name Springer, London
  • eBook Packages Computer Science
  • Print ISBN 978-1-4471-6295-7
  • Online ISBN 978-1-4471-6296-4
  • Series Print ISSN 2191-6586
  • Series Online ISSN 2191-6594
  • About this book