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Benchmarking Datasets for Human Activity Recognition

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Visual Analysis of Humans

Abstract

Recognizing human activities has become an important topic in the past few years. A variety of techniques for representing and modeling different human activities have been proposed, achieving reasonable performances in many scenarios. On the other hand, different benchmarks have also been collected and published. Different from other chapters focusing on the algorithmic aspects, this chapter gives an overview of different benchmarking datasets, summarizes the performances of the-state-of-the-art algorithms, and analyzes these datasets.

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Author information

Authors and Affiliations

  1. University of Washington, Seattle, WA, 98195, USA

    Haowei Liu & Ming-Ting Sun

  2. IBM T.J. Watson Research Center, Hawthorn, NY, 10532, USA

    Rogerio Feris

Authors
  1. Haowei Liu
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  2. Rogerio Feris
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  3. Ming-Ting Sun
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Corresponding author

Correspondence to Haowei Liu .

Editor information

Editors and Affiliations

  1. Department of Media Technology, Aalborg University, Niels Jernes Vej 14, Aalborg, 9220, Denmark

    Thomas B. Moeslund

  2. Centre for Vision, Speech & Signal Proc., University of Surrey, Guildford, GU2 7XH, Surrey, United Kingdom

    Adrian Hilton

  3. Copenhagen Institute of Technology, Aalborg University, Lautrupvang 2B, Ballerup, 2750, Denmark

    Volker Krüger

  4. Disney Research, Forbes Avenue 615, Pittsburgh, 15213, Pennsylvania, USA

    Leonid Sigal

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© 2011 Springer-Verlag London Limited

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Liu, H., Feris, R., Sun, MT. (2011). Benchmarking Datasets for Human Activity Recognition. In: Moeslund, T., Hilton, A., Krüger, V., Sigal, L. (eds) Visual Analysis of Humans. Springer, London. https://doi.org/10.1007/978-0-85729-997-0_20

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  • DOI: https://doi.org/10.1007/978-0-85729-997-0_20

  • Publisher Name: Springer, London

  • Print ISBN: 978-0-85729-996-3

  • Online ISBN: 978-0-85729-997-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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