Deep Learning on Lie Groups for Skeleton-based Action Recognition

Understand

In recent years, skeleton-based action recognition has become a popular 3D classification problem.

• State-of-the-art methods typically first represent each motion sequence as a high-dimensional trajectory on a Lie group with an additional dynamic time warping, and then shallowly learn favorable Lie group features.
• In this paper we incorporate the Lie group structure into a deep network architecture to learn more appropriate Lie group features for 3D action recognition.
• Within the network structure, we design rotation mapping layers to transform the input Lie group features into desirable ones, which are aligned better in the temporal domain.