  Graph-based reasoning over skeleton data has emerged as a promising approach
for human action recognition. However, the application of prior graph-based
methods, which predominantly employ whole temporal sequences as their input, to
the setting of online inference entails considerable computational redundancy.
In this paper, we tackle this issue by reformulating the Spatio-Temporal Graph
Convolutional Neural Network as a Continual Inference Network, which can
perform step-by-step predictions in time without repeat frame processing. To
evaluate our method, we create a continual version of ST-GCN, CoST-GCN,
alongside two derived methods with different self-attention mechanisms, CoAGCN
and CoS-TR. We investigate weight transfer strategies and architectural
modifications for inference acceleration, and perform experiments on the NTU
RGB+D 60, NTU RGB+D 120, and Kinetics Skeleton 400 datasets. Retaining similar
predictive accuracy, we observe up to 109x reduction in time complexity,
on-hardware accelerations of 26x, and reductions in maximum allocated memory of
52% during online inference.
