Human Activity Recognition: A Spatio-temporal Image Encoding of 3D Skeleton Data for Online Action Detection

Nassim Mokhtari, Alexis Nédélec, Pierre De Loor

2022

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.

Paper Citation

in Harvard Style

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, SciTePress, pages 448-455. DOI: 10.5220/0010835800003124

in Bibtex Style

@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},
}

in EndNote Style

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
AU - Mokhtari N.
AU - Nédélec A.
AU - De Loor P.
PY - 2022
SP - 448
EP - 455
DO - 10.5220/0010835800003124
PB - SciTePress