  We present a novel method for estimation of 3D human poses from a
multi-camera setup, employing distributed smart edge sensors coupled with a
backend through a semantic feedback loop. 2D joint detection for each camera
view is performed locally on a dedicated embedded inference processor. Only the
semantic skeleton representation is transmitted over the network and raw images
remain on the sensor board. 3D poses are recovered from 2D joints on a central
backend, based on triangulation and a body model which incorporates prior
knowledge of the human skeleton. A feedback channel from backend to individual
sensors is implemented on a semantic level. The allocentric 3D pose is
backprojected into the sensor views where it is fused with 2D joint detections.
The local semantic model on each sensor can thus be improved by incorporating
global context information. The whole pipeline is capable of real-time
operation. We evaluate our method on three public datasets, where we achieve
state-of-the-art results and show the benefits of our feedback architecture, as
well as in our own setup for multi-person experiments. Using the feedback
signal improves the 2D joint detections and in turn the estimated 3D poses.
