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Benchmarking for Person Re-identification

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Book cover Person Re-Identification

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

Abstract

The evaluation of computer vision and pattern recognition systems is usually a burdensome and time-consuming activity. In this chapter all the benchmarks publicly available for re-identification will be reviewed and compared, starting from the ancestors VIPeR and Caviar to the most recent datasets for 3D modeling such as SARC3d (with calibrated cameras) and RGBD-ID (with range sensors). Specific requirements and constraints are highlighted and reported for each of the described collections. In addition, details on the metrics that are mostly used to test and evaluate the re-identification systems are provided.

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Author information

Authors and Affiliations

  1. Dipartimento di Ingegneria “Enzo Ferrari”, Softech-ICT, University of Modena and Reggio Emilia, Modena, Italy

    Roberto Vezzani & Rita Cucchiara

Authors
  1. Roberto Vezzani
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  2. Rita Cucchiara
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Correspondence to Roberto Vezzani .

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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Vezzani, R., Cucchiara, R. (2014). Benchmarking 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_16

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  • DOI: https://doi.org/10.1007/978-1-4471-6296-4_16

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  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-6295-7

  • Online ISBN: 978-1-4471-6296-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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