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Robust Spatio-Temporal Features for Human Action Recognition

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Multimedia Analysis, Processing and Communications

Part of the book series: Studies in Computational Intelligence ((SCI,volume 346))

Abstract

In this chapter we describe and evaluate two recent feature detectors and descriptors used in the context of action recognition: 3D SIFT and 3D SURF. We first give an introduction to the algorithms in the 2D domain, named SIFT and SURF. For each method, an explanation of the theory upon which they are based is given and a comparison of the different approaches is shown. Then, we describe the extension of the 2D methods SIFT and SURF into the temporal domain, known as 3D SIFT and 3D SURF. The similarities and differences for both methods are emphasized. As a comparison of the 3D methods, we evaluate the performance of 3D SURF and 3D SIFT in the field of Human Action Recognition. Our results have shown similar accuracy performance, but a greater efficiency for 3D SURF approach compared with 3D SIFT.

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Author information

Authors and Affiliations

  1. The University of Trento, Italy

    Riccardo Mattivi

  2. The University of Sheffield, UK

    Ling Shao

Authors
  1. Riccardo Mattivi
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  2. Ling Shao
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Editor information

Editors and Affiliations

  1. School of Computer Engineering , Nanyang Technological University, 639798, Singapore

    Weisi Lin  & Dacheng Tao  & 

  2. Intelligent Systems Laboratory Systems Research Institute , Polish Academy of Sciences, Poland

    Janusz Kacprzyk

  3. Department of Computing , Hong Kong Polytechnic University, Hung Hom, Hong Kong

    Zhu Li

  4. School of Electronic Engineering and Computer Science, Queen Mary, University of London, London, U.K.

    Ebroul Izquierdo

  5. TCL-Thomson Electronics , Santa Clara, California

    Haohong Wang

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© 2011 Springer-Verlag Berlin Heidelberg

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Mattivi, R., Shao, L. (2011). Robust Spatio-Temporal Features for Human Action Recognition. In: Lin, W., Tao, D., Kacprzyk, J., Li, Z., Izquierdo, E., Wang, H. (eds) Multimedia Analysis, Processing and Communications. Studies in Computational Intelligence, vol 346. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19551-8_12

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  • DOI: https://doi.org/10.1007/978-3-642-19551-8_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-19550-1

  • Online ISBN: 978-3-642-19551-8

  • eBook Packages: EngineeringEngineering (R0)

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