The rapid growth of collaborative robotics in production requires new automation technologies that take human and machine equally into account. In this work, we describe a monocular camera based system to detect human-machine interactions from a bird's-eye perspective. Our system predicts poses of humans and robots from a single wide-angle color image. Even though our approach works on 2D color input, we lift the majority of detections to a metric 3D space. Our system merges pose information with predefined virtual sensors to coordinate human-machine interactions. We demonstrate the advantages of our system in three use cases.
@article{arxiv.1910.03239,
title = {Metric Pose Estimation for Human-Machine Interaction Using Monocular Vision},
author = {Christoph Heindl and Markus Ikeda and Gernot Stübl and Andreas Pichler and Josef Scharinger},
journal= {arXiv preprint arXiv:1910.03239},
year = {2019}
}