Real-time Multi-camera 3D Human Pose Estimation at the Edge for Industrial Applications

There is an increasing interest in exploiting human pose estimation (HPE) soft- ware in human-machine interaction systems. Nevertheless, adopting such a com- puter vision application in real industrial scenarios is challenging. To overcome occlusion limitations, it requires multiple cameras, which in turn require mul- tiple, distributed, and synchronized HPE software nodes running on resource- constrained edge devices. We address this challenge by presenting a real-time distributed 3D HPE platform, which consists of a set of 3D HPE software nodes on edge devices (i.e., one per camera) to redundantly extrapolate the human pose from different points of view. A centralized aggregator collects the pose information through a shared communication network and merges them, in real time, through a pipeline of filtering, clustering and association algorithms. It addresses network communication issues (e.g., delay and bandwidth variability) through a two-levels synchronization, and supports both single and multi-person pose estimation. We present the evaluation results with a real case of study (i.e., HPE for human-machine interaction in an intelligent manufacturing line), in which the platform accuracy and scalability are compared with state-of-the-art approaches and with a marker-based infra-red motion capture system.