  The raise of collaborative robotics has led to wide range of sensor
technologies to detect human-machine interactions: at short distances,
proximity sensors detect nontactile gestures virtually occlusion-free, while at
medium distances, active depth sensors are frequently used to infer human
intentions. We describe an optical system for large workspaces to capture human
pose based on a single panoramic color camera. Despite the two-dimensional
input, our system is able to predict metric 3D pose information over larger
field of views than would be possible with active depth measurement cameras. We
merge posture context with proximity perception to reduce occlusions and
improve accuracy at long distances. We demonstrate the capabilities of our
system in two use cases involving multiple humans and robots.
