  Current researches of action recognition mainly focus on single-view and
multi-view recognition, which can hardly satisfies the requirements of
human-robot interaction (HRI) applications to recognize actions from arbitrary
views. The lack of datasets also sets up barriers. To provide data for
arbitrary-view action recognition, we newly collect a large-scale RGB-D action
dataset for arbitrary-view action analysis, including RGB videos, depth and
skeleton sequences. The dataset includes action samples captured in 8 fixed
viewpoints and varying-view sequences which covers the entire 360 degree view
angles. In total, 118 persons are invited to act 40 action categories, and
25,600 video samples are collected. Our dataset involves more participants,
more viewpoints and a large number of samples. More importantly, it is the
first dataset containing the entire 360 degree varying-view sequences. The
dataset provides sufficient data for multi-view, cross-view and arbitrary-view
action analysis. Besides, we propose a View-guided Skeleton CNN (VS-CNN) to
tackle the problem of arbitrary-view action recognition. Experiment results
show that the VS-CNN achieves superior performance.
