  Comprehensive perception of human beings is the prerequisite to ensure the
safety of human-robot interaction. Currently, prevailing visual sensing
approach typically involves a single static camera, resulting in a restricted
and occluded field of view. In our work, we develop an active vision system
using multiple cameras to dynamically capture multi-source RGB-D data. An
integrated human sensing strategy based on a hierarchically connected tree
structure is proposed to fuse localized visual information. Constituting the
tree model are the nodes representing keypoints and the edges representing
keyparts, which are consistently interconnected to preserve the structural
constraints during multi-source fusion. Utilizing RGB-D data and HRNet, the 3D
positions of keypoints are analytically estimated, and their presence is
inferred through a sliding widow of confidence scores. Subsequently, the point
clouds of reliable keyparts are extracted by drawing occlusion-resistant masks,
enabling fine registration between data clouds and cylindrical model following
the hierarchical order. Experimental results demonstrate that our method
enhances keypart recognition recall from 69.20% to 90.10%, compared to
employing a single static camera. Furthermore, in overcoming challenges related
to localized and occluded perception, the robotic arm's obstacle avoidance
capabilities are effectively improved.
