loading
Papers

Research.Publish.Connect.

Paper

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

ISBN: 978-989-758-290-5

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 recog nition rates on all datasets outperform many existing methods and compete with the current state of the art techniques. (More)

PDF ImageFull Text

Download
CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 35.172.195.49

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

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 - Volume 5 VISAPP: VISAPP, ISBN 978-989-758-290-5, 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 - Volume 5 VISAPP: VISAPP,},
year={2018},
pages={343-350},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006625703430350},
isbn={978-989-758-290-5},
}

TY - CONF

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

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.