This paper proposes a novel system to estimate and track the 3D poses of multiple persons in calibrated RGB-Depth camera networks. The multi-view 3D pose of each person is computed by a central node which receives the single-view outcomes from each camera of the network. Each single-view outcome is computed by using a CNN for 2D pose estimation and extending the resulting skeletons to 3D by means of the sensor depth. The proposed system is marker-less, multi-person, independent of background and does not make any assumption on people appearance and initial pose. The system provides real-time outcomes, thus being perfectly suited for applications requiring user interaction. Experimental results show the effectiveness of this work with respect to a baseline multi-view approach in different scenarios. To foster research and applications based on this work, we released the source code in OpenPTrack, an open source project for RGB-D people tracking.
@article{arxiv.1710.06235,
title = {Real-time marker-less multi-person 3D pose estimation in RGB-Depth camera networks},
author = {Marco Carraro and Matteo Munaro and Jeff Burke and Emanuele Menegatti},
journal= {arXiv preprint arXiv:1710.06235},
year = {2017}
}
Comments
Submitted to the 2018 IEEE International Conference on Robotics and Automation