Efficient Multi-stream Temporal Learning and Post-fusion Strategy for 3D Skeleton-based Hand Activity Recognition

Yasser Boutaleb, Yasser Boutaleb, Catherine Soladie, Nam-Duong Duong, Amine Kacete, Jérôme Royan, Renaud Seguier

Abstract

Recognizing first-person hand activity is a challenging task, especially when not enough data are available. In this paper, we tackle this challenge by proposing a new hybrid learning pipeline for skeleton-based hand activity recognition, which is composed of three blocks. First, for a given sequence of hand’s joint positions, the spatial features are extracted using a dedicated combination of local and global spacial hand-crafted features. Then, the temporal dependencies are learned using a multi-stream learning strategy. Finally, a hand activity sequence classifier is learned, via our Post-fusion strategy, applied to the previously learned temporal dependencies. The experiments, evaluated on two real-world data sets, show that our approach performs better than the state-of-the-art approaches. For more ablation study, we compared our Post-fusion strategy with three traditional fusion baselines and showed an improvement above 2.4% of accuracy.

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Paper Citation


in Harvard Style

Boutaleb Y., Soladie C., Duong N., Kacete A., Royan J. and Seguier R. (2021). Efficient Multi-stream Temporal Learning and Post-fusion Strategy for 3D Skeleton-based Hand Activity Recognition.In Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, ISBN 978-989-758-488-6, pages 293-302. DOI: 10.5220/0010232702930302


in Bibtex Style

@conference{visapp21,
author={Yasser Boutaleb and Catherine Soladie and Nam-Duong Duong and Amine Kacete and Jérôme Royan and Renaud Seguier},
title={Efficient Multi-stream Temporal Learning and Post-fusion Strategy for 3D Skeleton-based Hand Activity Recognition},
booktitle={Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,},
year={2021},
pages={293-302},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010232702930302},
isbn={978-989-758-488-6},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,
TI - Efficient Multi-stream Temporal Learning and Post-fusion Strategy for 3D Skeleton-based Hand Activity Recognition
SN - 978-989-758-488-6
AU - Boutaleb Y.
AU - Soladie C.
AU - Duong N.
AU - Kacete A.
AU - Royan J.
AU - Seguier R.
PY - 2021
SP - 293
EP - 302
DO - 10.5220/0010232702930302