Multi-camera Torso Pose Estimation using Graph Neural Networks

Abstract

Estimating the location and orientation of humans is an essential skill for service and assistive robots. To achieve a reliable estimation in a wide area such as an apartment, multiple RGBD cameras are frequently used. Firstly, these setups are relatively expensive. Secondly, they seldom perform an effective data fusion using the multiple camera sources at an early stage of the processing pipeline. Occlusions and partial views make this second point very relevant in these scenarios. The proposal presented in this paper makes use of graph neural networks to merge the information acquired from multiple camera sources, achieving a mean absolute error below 125mm for the location and 10 • for the orientation using low-resolution RGB images. The experiments, conducted in an apartment with three cameras, benchmarked two different graph neural network implementations and a third architecture based on fully connected layers. The software used has been released as open-source in a public repository 1 .

A graph neural network to model disruption in human-aware robot navigation

“…Based on the assumption that in real life scenarios we can build on top of third party body trackers (e.g., [31,34]) and path planning systems, we proceed with the evaluation of the approach against the dataset presented in Section 3. Because all nodes are connected to their corresponding room node, the GNNs were trained to perform backpropagation based on the feature vector of the room node in the last layer.…”