  Graph convolutional networks (GCNs) can effectively capture the features of
related nodes and improve the performance of the model. More attention is paid
to employing GCN in Skeleton-Based action recognition. But existing methods
based on GCNs have two problems. First, the consistency of temporal and spatial
features is ignored for extracting features node by node and frame by frame. To
obtain spatiotemporal features simultaneously, we design a generic
representation of skeleton sequences for action recognition and propose a novel
model called Temporal Graph Networks (TGN). Secondly, the adjacency matrix of
the graph describing the relation of joints is mostly dependent on the physical
connection between joints. To appropriately describe the relations between
joints in the skeleton graph, we propose a multi-scale graph strategy, adopting
a full-scale graph, part-scale graph, and core-scale graph to capture the local
features of each joint and the contour features of important joints.
Experiments were carried out on two large datasets and results show that TGN
with our graph strategy outperforms state-of-the-art methods.
