  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. The proposed AGC-LSTM can not only capture
discriminative features in spatial configuration and temporal dynamics but also
explore the co-occurrence relationship between spatial and temporal domains. We
also present a temporal hierarchical architecture to increases temporal
receptive fields of the top AGC-LSTM layer, which boosts the ability to learn
the high-level semantic representation and significantly reduces the
computation cost. Furthermore, to select discriminative spatial information,
the attention mechanism is employed to enhance information of key joints in
each AGC-LSTM layer. Experimental results on two datasets are provided: NTU
RGB+D dataset and Northwestern-UCLA dataset. The comparison results demonstrate
the effectiveness of our approach and show that our approach outperforms the
state-of-the-art methods on both datasets.
