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

10 Feb 2021archive 2025-07-28

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

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 spatial 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 studies, we compared our Post-fusion strategy with three traditional fusion baselines and showed an improvement above 2.4% of accuracy.

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