An Attention Enhanced Graph Convolutional LSTM Network for Skeleton-Based Action Recognition

Understand

Skeleton-based action recognition is an important task that requires the adequate understanding of movement characteristics of a human action from the given skeleton sequence.

• Recent studies have shown that exploring spatial and temporal features of the skeleton sequence is vital for this task.
• Nevertheless, how to effectively extract discriminative spatial and temporal features is still a challenging problem.
• In this paper, we propose a novel Attention Enhanced Graph Convolutional LSTM Network (AGC-LSTM) for human action recognition from skeleton data.