  Skeleton-based action recognition has attracted research attentions in recent
years. One common drawback in currently popular skeleton-based human action
recognition methods is that the sparse skeleton information alone is not
sufficient to fully characterize human motion. This limitation makes several
existing methods incapable of correctly classifying action categories which
exhibit only subtle motion differences. In this paper, we propose a novel
framework for employing human pose skeleton and joint-centered light-weight
information jointly in a two-stream graph convolutional network, namely,
JOLO-GCN. Specifically, we use Joint-aligned optical Flow Patches (JFP) to
capture the local subtle motion around each joint as the pivotal joint-centered
visual information. Compared to the pure skeleton-based baseline, this hybrid
scheme effectively boosts performance, while keeping the computational and
memory overheads low. Experiments on the NTU RGB+D, NTU RGB+D 120, and the
Kinetics-Skeleton dataset demonstrate clear accuracy improvements attained by
the proposed method over the state-of-the-art skeleton-based methods.
