  With the advances in capturing 2D or 3D skeleton data, skeleton-based action
recognition has received an increasing interest over the last years. As
skeleton data is commonly represented by graphs, graph convolutional networks
have been proposed for this task. While current graph convolutional networks
accurately recognize actions, they are too expensive for robotics applications
where limited computational resources are available. In this paper, we
therefore propose a highly efficient graph convolutional network that addresses
the limitations of previous works. This is achieved by a parallel structure
that gradually fuses motion and spatial information and by reducing the
temporal resolution as early as possible. Furthermore, we explicitly address
the issue that human poses can contain errors. To this end, the network first
refines the poses before they are further processed to recognize the action. We
therefore call the network Pose Refinement Graph Convolutional Network.
Compared to other graph convolutional networks, our network requires 86\%-93\%
less parameters and reduces the floating point operations by 89%-96% while
achieving a comparable accuracy. It therefore provides a much better trade-off
between accuracy, memory footprint and processing time, which makes it suitable
for robotics applications.
