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Efficient Pose-Based Action Recognition

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Computer Vision -- ACCV 2014 (ACCV 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9007))

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Abstract

Action recognition from 3d pose data has gained increasing attention since the data is readily available for depth or RGB-D videos. The most successful approaches so far perform an expensive feature selection or mining approach for training. In this work, we introduce an algorithm that is very efficient for training and testing. The main idea is that rich structured data like 3d pose does not require sophisticated feature modeling or learning. Instead, we reduce pose data over time to histograms of relative location, velocity, and their correlations and use partial least squares to learn a compact and discriminative representation from it. Despite of its efficiency, our approach achieves state-of-the-art accuracy on four different benchmarks. We further investigate differences of 2d and 3d pose data for action recognition.

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Notes

  1. 1.

    http://research.microsoft.com/en-us/um/people/zliu/ActionRecoRsrc/.

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Acknowledgment

This work was carried out in the project automatic activity recognition in large image databases which is funded by the German Research Foundation (DFG). The authors would also like to acknowledge the financial support provided by the DFG Emmy Noether program (GA 1927/1-1).

Author information

Authors and Affiliations

  1. Bonn-Aachen International Center for IT, University of Bonn, Bonn, Germany

    Abdalrahman Eweiwi, Muhammed S. Cheema & Christian Bauckhage

  2. Computer Vision Group, University of Bonn, Bonn, Germany

    Juergen Gall

  3. Multimedia Pattern Recognition Group, Fraunhofer IAIS, Sankt Augustin, Germany

    Christian Bauckhage

Authors
  1. Abdalrahman Eweiwi
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  2. Muhammed S. Cheema
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  3. Christian Bauckhage
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  4. Juergen Gall
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Corresponding author

Correspondence to Abdalrahman Eweiwi .

Editor information

Editors and Affiliations

  1. Technische Universität München, Garching, Germany

    Daniel Cremers

  2. University of Adelaide, Adelaide, South Australia, Australia

    Ian Reid

  3. Keio University, Yokohama, Kanagawa, Japan

    Hideo Saito

  4. University of California at Merced, Merced, California, USA

    Ming-Hsuan Yang

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Eweiwi, A., Cheema, M.S., Bauckhage, C., Gall, J. (2015). Efficient Pose-Based Action Recognition. In: Cremers, D., Reid, I., Saito, H., Yang, MH. (eds) Computer Vision -- ACCV 2014. ACCV 2014. Lecture Notes in Computer Science(), vol 9007. Springer, Cham. https://doi.org/10.1007/978-3-319-16814-2_28

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  • DOI: https://doi.org/10.1007/978-3-319-16814-2_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16813-5

  • Online ISBN: 978-3-319-16814-2

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