  We study the performance of state-of-the-art human keypoint detectors in the
context of close proximity human-robot interaction. The detection in this
scenario is specific in that only a subset of body parts such as hands and
torso are in the field of view. In particular, (i) we survey existing datasets
with human pose annotation from the perspective of close proximity images and
prepare and make publicly available a new Human in Close Proximity (HiCP)
dataset; (ii) we quantitatively and qualitatively compare state-of-the-art
human whole-body 2D keypoint detection methods (OpenPose, MMPose, AlphaPose,
Detectron2) on this dataset; (iii) since accurate detection of hands and
fingers is critical in applications with handovers, we evaluate the performance
of the MediaPipe hand detector; (iv) we deploy the algorithms on a humanoid
robot with an RGB-D camera on its head and evaluate the performance in 3D human
keypoint detection. A motion capture system is used as reference.
  The best performing whole-body keypoint detectors in close proximity were
MMPose and AlphaPose, but both had difficulty with finger detection. Thus, we
propose a combination of MMPose or AlphaPose for the body and MediaPipe for the
hands in a single framework providing the most accurate and robust detection.
We also analyse the failure modes of individual detectors -- for example, to
what extent the absence of the head of the person in the image degrades
performance. Finally, we demonstrate the framework in a scenario where a
humanoid robot interacting with a person uses the detected 3D keypoints for
whole-body avoidance maneuvers.
