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Authors: Nassim Mokhtari ; Alexis Nédélec and Pierre De Loor

Affiliation: Lab-STICC (CNRS UMR 6285), ENIB, Centre Européen de Réalité Virtuelle, Brest, France

Keyword(s): 3D Skeleton Data, Spatio-temporal Image Encoding, Sliding Window, Online Action Recognition, Human Activity Recognition, Deep Learning.

Abstract: Human activity recognition (HAR) based on skeleton data that can be extracted from videos (Kinect for example) , or provided by a depth camera is a time series classification problem, where handling both spatial and temporal dependencies is a crucial task, in order to achieve a good recognition. In the online human activity recognition, identifying the beginning and end of an action is an important element, that might be difficult in a continuous data flow. In this work, we present a 3D skeleton data encoding method to generate an image that preserves the spatial and temporal dependencies existing between the skeletal joints.To allow online action detection we combine this encoding system with a sliding window on the continous data stream. By this way, no start or stop timestamp is needed and the recognition can be done at any moment. A deep learning CNN algorithm is used to achieve actions online detection.

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Paper citation in several formats:
Mokhtari, N.; Nédélec, A. and De Loor, P. (2022). Human Activity Recognition: A Spatio-temporal Image Encoding of 3D Skeleton Data for Online Action Detection. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP; ISBN 978-989-758-555-5; ISSN 2184-4321, SciTePress, pages 448-455. DOI: 10.5220/0010835800003124

@conference{visapp22,
author={Nassim Mokhtari. and Alexis Nédélec. and Pierre {De Loor}.},
title={Human Activity Recognition: A Spatio-temporal Image Encoding of 3D Skeleton Data for Online Action Detection},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP},
year={2022},
pages={448-455},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010835800003124},
isbn={978-989-758-555-5},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP
TI - Human Activity Recognition: A Spatio-temporal Image Encoding of 3D Skeleton Data for Online Action Detection
SN - 978-989-758-555-5
IS - 2184-4321
AU - Mokhtari, N.
AU - Nédélec, A.
AU - De Loor, P.
PY - 2022
SP - 448
EP - 455
DO - 10.5220/0010835800003124
PB - SciTePress