グラフニューラルネットワークによる複数カメラからの胴体姿勢推定
Multi-camera Torso Pose Estimation using Graph Neural Networks
複数のRGBDカメラ映像をグラフニューラルネットワークで初期段階から統合し、遮蔽や部分視野があっても人物の位置と向きを高精度に推定する手法を提案した。
著者: Daniel Rodriguez-Criado, Pilar Bachiller, Pablo Bustos, George Vogiatzis, Luis J. Manso
分類: cs.RO, cs.CV, cs.LG
原文アブストラクト
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 125 mm for the location and 10 degrees 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 (https://github.com/vangiel/WheresTheFellow).
