  Current methods for skeleton-based human action recognition usually work with
complete skeletons. However, in real scenarios, it is inevitable to capture
incomplete or noisy skeletons, which could significantly deteriorate the
performance of current methods when some informative joints are occluded or
disturbed. To improve the robustness of action recognition models, a
multi-stream graph convolutional network (GCN) is proposed to explore
sufficient discriminative features spreading over all skeleton joints, so that
the distributed redundant representation reduces the sensitivity of the action
models to non-standard skeletons. Concretely, the backbone GCN is extended by a
series of ordered streams which is responsible for learning discriminative
features from the joints less activated by preceding streams. Here, the
activation degrees of skeleton joints of each GCN stream are measured by the
class activation maps (CAM), and only the information from the unactivated
joints will be passed to the next stream, by which rich features over all
active joints are obtained. Thus, the proposed method is termed richly
activated GCN (RA-GCN). Compared to the state-of-the-art (SOTA) methods, the
RA-GCN achieves comparable performance on the standard NTU RGB+D 60 and 120
datasets. More crucially, on the synthetic occlusion and jittering datasets,
the performance deterioration due to the occluded and disturbed joints can be
significantly alleviated by utilizing the proposed RA-GCN.
