About: Multi-camera Torso Pose Estimation using Graph Neural Networks

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Multi-camera Torso Pose Estimation using Graph Neural Networks
Creator	Manso, Luis Vogiatzis, George Bachiller, Pilar Bustos, Pablo Rodriguez-Criado, Daniel
source	ArXiv
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 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).
has issue date	2020-07-28(xsd:dateTime)
has license	arxiv
sha1sum (hex)	bb057f16e37a3e3e4c6fa3945a10cdc1f044b2b5
resource representing a document's title	Multi-camera Torso Pose Estimation using Graph Neural Networks
resource representing a document's body	covid:bb057f16e37a3e3e4c6fa3945a10cdc1f044b2b5#body_text
is schema:about of	named entity 'area' named entity 'perform' named entity 'RGB' named entity 'released' named entity 'Torso' »more»

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