Human action recognition plays an important role when developing intelligent interactions between humans and machines. While there is a lot of active research on improving the machine learning algorithms for skeleton-based action recognition, not much attention has been given to the quality of the input skeleton data itself. This work demonstrates that by making use of multiple camera views to triangulate more accurate 3D~skeletons, the performance of state-of-the-art action recognition models can be improved significantly. This suggests that the quality of the input data is currently a limiting factor for the performance of these models. Based on these results, it is argued that the cost-benefit ratio of using multiple cameras is very favorable in most practical use-cases, therefore future research in skeleton-based action recognition should consider multi-view applications as the standard setup.

翻译：人体动作识别在人机智能交互的发展中扮演着重要角色。尽管当前有大量研究致力于改进基于骨架的动作识别机器学习算法，但输入骨架数据本身的质量却鲜少受到关注。本研究表明，通过利用多个摄像头视角三角测量出更精确的三维骨架，可以显著提升现有最优动作识别模型的性能。这表明输入数据的质量目前是制约这些模型性能的关键因素。基于这些结果，本文认为多摄像头方案在大多数实际应用场景中具有极佳的成本效益比，因此未来基于骨架的动作识别研究应将多视角应用作为标准设置。