Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition

Understand

Dynamics of human body skeletons convey significant information for human action recognition.

• Conventional approaches for modeling skeletons usually rely on hand-crafted parts or traversal rules, thus resulting in limited expressive power and difficulties of generalization.
• In this work, we propose a novel model of dynamic skeletons called Spatial-Temporal Graph Convolutional Networks (ST-GCN), which moves beyond the limitations of previous methods by automatically learning both the spatial and temporal patterns from data.
• This formulation not only leads to greater expressive power but also stronger generalization capability.