Domain and View-point Agnostic Hand Action Recognition

3 Mar 2021  ·  Alberto Sabater, Iñigo Alonso, Luis Montesano, Ana C. Murillo ·

Hand action recognition is a special case of action recognition with applications in human-robot interaction, virtual reality or life-logging systems. Building action classifiers able to work for such heterogeneous action domains is very challenging. There are very subtle changes across different actions from a given application but also large variations across domains (e.g. virtual reality vs life-logging). This work introduces a novel skeleton-based hand motion representation model that tackles this problem. The framework we propose is agnostic to the application domain or camera recording view-point. When working on a single domain (intra-domain action classification) our approach performs better or similar to current state-of-the-art methods on well-known hand action recognition benchmarks. And, more importantly, when performing hand action recognition for action domains and camera perspectives which our approach has not been trained for (cross-domain action classification), our proposed framework achieves comparable performance to intra-domain state-of-the-art methods. These experiments show the robustness and generalization capabilities of our framework.

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Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Skeleton Based Action Recognition First-Person Hand Action Benchmark TCN-Summ 1:3 Accuracy 92.9 # 1
1:1 Accuracy 95.93 # 2
3:1 Accuracy 96.76 # 1
Cross-person Accuracy 88.70 # 1
Skeleton Based Action Recognition SHREC 2017 track on 3D Hand Gesture Recognition TCN-Summ 28 gestures accuracy 91.43 # 2
14 gestures accuracy 93.57 # 3


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