Skeleton-Based Action Recognition with Spatial Reasoning and Temporal Stack Learning

Understand

Skeleton-based action recognition has made great progress recently, but many problems still remain unsolved.

• For example, most of the previous methods model the representations of skeleton sequences without abundant spatial structure information and detailed temporal dynamics features.
• In this paper, we propose a novel model with spatial reasoning and temporal stack learning (SR-TSL) for skeleton based action recognition, which consists of a spatial reasoning network (SRN) and a temporal stack learning network (TSLN).
• The SRN can capture the high-level spatial structural information within each frame by a residual graph neural network, while the TSLN can model the detailed temporal dynamics of skeleton sequences by a composition of multiple skip-clip LSTMs.