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Authors: Hany A. El-Ghaish 1 ; Amin Shoukry 2 and Mohamed E. Hussein 3

Affiliations: 1 Egypt-Japan University of Science and Technology, Egypt ; 2 Egypt-Japan University of Science and Technology, Faculty of Engineering and Alexandria University, Egypt ; 3 Information Sciences Institute , Faculty of Engineering and Alexandria University, United States

Keyword(s): Hand-crafted Features, Covariance Descriptor, Skeleton-based Human Action Recognition.

Abstract: A highly discriminative and computationally efficient descriptor is needed in many computer vision applications involving human action recognition. This paper proposes a hand-crafted skeleton-based descriptor for human action recognition. It is constructed from five fixed size covariance matrices calculated using strongly related joints coordinates over five body parts (spine, left/ right arms, and left/ right legs). Since covariance matrices are symmetric, the lower/ upper triangular parts of these matrices are concatenated to generate an efficient descriptor. It achieves a saving from 78.26 % to 80.35 % in storage space and from 75 % to 90 % in processing time (depending on the dataset) relative to techniques adopting a covariance descriptor based on all the skeleton joints. To show the effectiveness of the proposed method, its performance is evaluated on five public datasets: MSR-Action3D, MSRC-12 Kinect Gesture, UTKinect-Action, Florence3D-Action, and NTU RGB+D. The obtained reco gnition rates on all datasets outperform many existing methods and compete with the current state of the art techniques. (More)

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Paper citation in several formats:
El-Ghaish, H.; Shoukry, A. and Hussein, M. (2018). CovP3DJ: Skeleton-parts-based-covariance Descriptor for Human Action Recognition. In Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 5: VISAPP; ISBN 978-989-758-290-5; ISSN 2184-4321, SciTePress, pages 343-350. DOI: 10.5220/0006625703430350

@conference{visapp18,
author={Hany A. El{-}Ghaish. and Amin Shoukry. and Mohamed E. Hussein.},
title={CovP3DJ: Skeleton-parts-based-covariance Descriptor for Human Action Recognition},
booktitle={Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 5: VISAPP},
year={2018},
pages={343-350},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006625703430350},
isbn={978-989-758-290-5},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 5: VISAPP
TI - CovP3DJ: Skeleton-parts-based-covariance Descriptor for Human Action Recognition
SN - 978-989-758-290-5
IS - 2184-4321
AU - El-Ghaish, H.
AU - Shoukry, A.
AU - Hussein, M.
PY - 2018
SP - 343
EP - 350
DO - 10.5220/0006625703430350
PB - SciTePress

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