Proxemics Toolkit For F-formation Patterns Detection

Interactions between people are of utmost magnitude for cross-device systems development. By using this kind of software, devices owned by those people end up interacting between themselves, and, therefore, making the system work. This work proposes to elaborate a toolkit that can detect and analyze...

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Autores principales:	Alfredo Barrientos, Miguel Eduardo Cuadros Galvez, Mauricio Rivas, Paul Alvarez
Formato:	article
Lenguaje:	EN
Publicado:	FRUCT 2021
Materias:	f-formation patterns proxemics human detection human interactions cross-device interactions Telecommunication TK5101-6720
Acceso en línea:	https://doaj.org/article/49c3869329b349a3b1ce3bebd8e86086
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id	oai:doaj.org-article:49c3869329b349a3b1ce3bebd8e86086
record_format	dspace
spelling	oai:doaj.org-article:49c3869329b349a3b1ce3bebd8e860862021-11-20T15:59:33ZProxemics Toolkit For F-formation Patterns Detection2305-72542343-073710.23919/FRUCT53335.2021.9599996https://doaj.org/article/49c3869329b349a3b1ce3bebd8e860862021-10-01T00:00:00Zhttps://www.fruct.org/publications/fruct30/files/Riv.pdfhttps://doaj.org/toc/2305-7254https://doaj.org/toc/2343-0737Interactions between people are of utmost magnitude for cross-device systems development. By using this kind of software, devices owned by those people end up interacting between themselves, and, therefore, making the system work. This work proposes to elaborate a toolkit that can detect and analyze those human interactions by using computer vision over videos showing them. All of these through the usage of 3D modeled test scenarios in addition to applying proxemics metrics and concepts of F-formations patterns so we can define them at various interaction types. To meet this goal, we used a previously trained human detection model in conjunction with two proposed concepts to estimate indispensable values: Distance between people, their body orientation, and relative position. To validate this tool, we tested it with a hundred test cases, each one having a set of different F-formation types so we could get the effectiveness of its detection functionality.Alfredo BarrientosMiguel Eduardo Cuadros GalvezMauricio RivasPaul AlvarezFRUCTarticlef-formation patternsproxemicshuman detectionhuman interactionscross-device interactionsTelecommunicationTK5101-6720ENProceedings of the XXth Conference of Open Innovations Association FRUCT, Vol 30, Iss 1, Pp 216-222 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	f-formation patterns proxemics human detection human interactions cross-device interactions Telecommunication TK5101-6720
spellingShingle	f-formation patterns proxemics human detection human interactions cross-device interactions Telecommunication TK5101-6720 Alfredo Barrientos Miguel Eduardo Cuadros Galvez Mauricio Rivas Paul Alvarez Proxemics Toolkit For F-formation Patterns Detection
description	Interactions between people are of utmost magnitude for cross-device systems development. By using this kind of software, devices owned by those people end up interacting between themselves, and, therefore, making the system work. This work proposes to elaborate a toolkit that can detect and analyze those human interactions by using computer vision over videos showing them. All of these through the usage of 3D modeled test scenarios in addition to applying proxemics metrics and concepts of F-formations patterns so we can define them at various interaction types. To meet this goal, we used a previously trained human detection model in conjunction with two proposed concepts to estimate indispensable values: Distance between people, their body orientation, and relative position. To validate this tool, we tested it with a hundred test cases, each one having a set of different F-formation types so we could get the effectiveness of its detection functionality.
format	article
author	Alfredo Barrientos Miguel Eduardo Cuadros Galvez Mauricio Rivas Paul Alvarez
author_facet	Alfredo Barrientos Miguel Eduardo Cuadros Galvez Mauricio Rivas Paul Alvarez
author_sort	Alfredo Barrientos
title	Proxemics Toolkit For F-formation Patterns Detection
title_short	Proxemics Toolkit For F-formation Patterns Detection
title_full	Proxemics Toolkit For F-formation Patterns Detection
title_fullStr	Proxemics Toolkit For F-formation Patterns Detection
title_full_unstemmed	Proxemics Toolkit For F-formation Patterns Detection
title_sort	proxemics toolkit for f-formation patterns detection
publisher	FRUCT
publishDate	2021
url	https://doaj.org/article/49c3869329b349a3b1ce3bebd8e86086
work_keys_str_mv	AT alfredobarrientos proxemicstoolkitforfformationpatternsdetection AT migueleduardocuadrosgalvez proxemicstoolkitforfformationpatternsdetection AT mauriciorivas proxemicstoolkitforfformationpatternsdetection AT paulalvarez proxemicstoolkitforfformationpatternsdetection
_version_	1718419420506750976

Proxemics Toolkit For F-formation Patterns Detection

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