Real-time marker-less multi-person 3D pose estimation in RGB-Depth camera networks
This work addresses the problem of enabling user interaction in applications like robotics or surveillance by providing a practical, open-source solution for multi-person 3D pose estimation, though it is incremental as it builds on existing CNN and depth-sensing methods.
The paper tackles real-time 3D pose estimation for multiple people using RGB-Depth camera networks, achieving marker-less tracking independent of background and appearance with real-time performance, as demonstrated in experiments against a baseline multi-view approach.
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.