  In skeleton-based action recognition, graph convolutional networks (GCNs),
which model the human body skeletons as spatiotemporal graphs, have achieved
remarkable performance. However, in existing GCN-based methods, the topology of
the graph is set manually, and it is fixed over all layers and input samples.
This may not be optimal for the hierarchical GCN and diverse samples in action
recognition tasks. In addition, the second-order information (the lengths and
directions of bones) of the skeleton data, which is naturally more informative
and discriminative for action recognition, is rarely investigated in existing
methods. In this work, we propose a novel two-stream adaptive graph
convolutional network (2s-AGCN) for skeleton-based action recognition. The
topology of the graph in our model can be either uniformly or individually
learned by the BP algorithm in an end-to-end manner. This data-driven method
increases the flexibility of the model for graph construction and brings more
generality to adapt to various data samples. Moreover, a two-stream framework
is proposed to model both the first-order and the second-order information
simultaneously, which shows notable improvement for the recognition accuracy.
Extensive experiments on the two large-scale datasets, NTU-RGBD and
Kinetics-Skeleton, demonstrate that the performance of our model exceeds the
state-of-the-art with a significant margin.
