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Slope Pattern Spectra for Human Action Recognition

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Image Analysis and Recognition (ICIAR 2018)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10882))

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Abstract

Motion history image (MHI) is widely used for human action recognition (HAR) due to its simple representation of the motion information. MHI has also been used in combination with other feature extraction techniques to recognize human actions. However, there is still room for improvement. Therefore, this paper proposes a method that includes a holistic feature extraction technique not yet employed in HAR applications, named slope pattern spectra (SPS). We extract increasing slope pattern spectra from motion history images and feed them into a K-Nearest Neighbor (KNN) classifier in order to recognize various human actions. Our proposed framework has been tested on the KTH dataset, a commonly used benchmark dataset for HAR. Experimental results demonstrate that SPS are suitable feature descriptors for HAR via MHI.

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Author information

Authors and Affiliations

  1. School of Engineering, University of KwaZulu-Natal, Durban, South Africa

    Ignace Tchangou Toudjeu & Jules Raymond Tapamo

Authors
  1. Ignace Tchangou Toudjeu
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  2. Jules Raymond Tapamo
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Corresponding author

Correspondence to Ignace Tchangou Toudjeu .

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Editors and Affiliations

  1. University of Porto, Porto, Portugal

    Aurélio Campilho

  2. University of Waterloo, Waterloo, Ontario, Canada

    Fakhri Karray

  3. Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands

    Bart ter Haar Romeny

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Toudjeu, I.T., Tapamo, J.R. (2018). Slope Pattern Spectra for Human Action Recognition. In: Campilho, A., Karray, F., ter Haar Romeny, B. (eds) Image Analysis and Recognition. ICIAR 2018. Lecture Notes in Computer Science(), vol 10882. Springer, Cham. https://doi.org/10.1007/978-3-319-93000-8_43

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  • DOI: https://doi.org/10.1007/978-3-319-93000-8_43

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-92999-6

  • Online ISBN: 978-3-319-93000-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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