Skarimva: Skeleton-based Action Recognition is a Multi-view Application
This work addresses the problem of improving the accuracy of skeleton-based action recognition for human-machine interaction systems by focusing on input data quality, suggesting an incremental improvement for existing methods.
This paper demonstrates that the performance of state-of-the-art skeleton-based action recognition models can be significantly improved by using multiple camera views to triangulate more accurate 3D skeletons. This indicates that input data quality is a limiting factor for current models.
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.