View-invariant 3D Skeleton-based Human Activity Recognition based on Transformer and Spatio-temporal Features

Ahmed Snoun, Tahani Bouchrika, Olfa Jemai

2022

Abstract

With the emergence of depth sensors, real-time 3D human skeleton estimation have become easier to accomplish. Thus, methods for human activity recognition (HAR) based on 3D skeleton have become increasingly accessible. In this paper, we introduce a new approach for human activity recognition using 3D skeletal data. Our approach generates a set of spatio-temporal and view-invariant features from the skeleton joints. Then, the extracted features are analyzed using a typical Transformer encoder in order to recognize the activity. In fact, Transformers, which are based on self-attention mechanism, have been successful in many domains in the last few years, which makes them suitable for HAR. The proposed approach shows promising performance on different well-known datasets that provide 3D skeleton data, namely, KARD, Florence 3D, UTKinect Action 3D and MSR Action 3D.

Paper Citation

in Harvard Style

Snoun A., Bouchrika T. and Jemai O. (2022). View-invariant 3D Skeleton-based Human Activity Recognition based on Transformer and Spatio-temporal Features. In Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-549-4, pages 706-715. DOI: 10.5220/0010895300003122

in Bibtex Style

@conference{icpram22,
author={Ahmed Snoun and Tahani Bouchrika and Olfa Jemai},
title={View-invariant 3D Skeleton-based Human Activity Recognition based on Transformer and Spatio-temporal Features},
booktitle={Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2022},
pages={706-715},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010895300003122},
isbn={978-989-758-549-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - View-invariant 3D Skeleton-based Human Activity Recognition based on Transformer and Spatio-temporal Features
SN - 978-989-758-549-4
AU - Snoun A.
AU - Bouchrika T.
AU - Jemai O.
PY - 2022
SP - 706
EP - 715
DO - 10.5220/0010895300003122