Real-Time Multi-View 3D Human Pose Estimation using Semantic Feedback to Smart Edge Sensors
This addresses the problem of efficient and accurate 3D pose estimation for applications like surveillance or human-computer interaction, though it is incremental as it builds on existing methods with a feedback enhancement.
The paper tackles real-time 3D human pose estimation from multi-camera setups by using smart edge sensors with a semantic feedback loop, achieving state-of-the-art results on public datasets and improving 2D joint detections and 3D pose accuracy.
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.