  Skeleton-based action recognition, as a subarea of action recognition, is
swiftly accumulating attention and popularity. The task is to recognize actions
performed by human articulation points. Compared with other data modalities, 3D
human skeleton representations have extensive unique desirable characteristics,
including succinctness, robustness, racial-impartiality, and many more. We aim
to provide a roadmap for new and existing researchers a on the landscapes of
skeleton-based action recognition for new and existing researchers. To this
end, we present a review in the form of a taxonomy on existing works of
skeleton-based action recognition. We partition them into four major
categories: (1) datasets; (2) extracting spatial features; (3) capturing
temporal patterns; (4) improving signal quality. For each method, we provide
concise yet informatively-sufficient descriptions. To promote more fair and
comprehensive evaluation on existing approaches of skeleton-based action
recognition, we collect ANUBIS, a large-scale human skeleton dataset. Compared
with previously collected dataset, ANUBIS are advantageous in the following
four aspects: (1) employing more recently released sensors; (2) containing
novel back view; (3) encouraging high enthusiasm of subjects; (4) including
actions of the COVID pandemic era. Using ANUBIS, we comparably benchmark
performance of current skeleton-based action recognizers. At the end of this
paper, we outlook future development of skeleton-based action recognition by
listing several new technical problems. We believe they are valuable to solve
in order to commercialize skeleton-based action recognition in the near future.
The dataset of ANUBIS is available at:
http://hcc-workshop.anu.edu.au/webs/anu101/home.
