Visual Recognition to Access and Analyze People Density and Flow Patterns in Indoor Environments

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dc.contributor.authorRuz Ruz, Cristian Daniel
dc.contributor.authorPieringer Baeza, Christian Philip
dc.contributor.authorPeralta Marquez, Billy Mark
dc.contributor.authorLillo Valles, Iván Alberto
dc.contributor.authorEspinace Ronda, Pablo Andrés
dc.contributor.authorGonzalez, R.
dc.contributor.authorWendt González, Bruno Nicolás
dc.contributor.authorMery Quiroz, Domingo Arturo
dc.contributor.authorSoto Arriaza, Álvaro
dc.date.accessioned2022-05-11T20:05:45Z
dc.date.available2022-05-11T20:05:45Z
dc.date.issued2015
dc.description.abstractThis work describes our experience developing a system to access density and flow of people in large indoor spaces using a network of RGB cameras. The proposed system is based on a set of overlapped and calibrated cameras. This facilitates the use of geometric constraints that help to reduce visual ambiguities. These constraints are combined with classifiers based on visual appearance to produce an efficient and robust method to detect and track humans. In this work, we argue that flow and density of people are low level measurements that need to be complemented with suitable analytic tools to bridge semantic gaps and become useful information for a target application. Consequently, we also propose a set of analytic tools that help a human user to effectively take advantage of the measurements provided by the system. Finally, we report results that demonstrate the relevance of the proposed ideas.
dc.fuente.origenIEEE
dc.identifier.doi10.1109/WACV.2015.8
dc.identifier.isbn978-1479966837
dc.identifier.issn1550-5790
dc.identifier.urihttps://doi.org/10.1109/WACV.2015.8
dc.identifier.urihttps://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7045862
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/63768
dc.information.autorucEscuela de ingeniería ; Ruz Ruz, Cristian Daniel ; S/I ; 12357
dc.information.autorucEscuela de ingeniería ; Pieringer Baeza, Christian Philip ; S/I ; 169967
dc.information.autorucEscuela de ingeniería ; Peralta Marquez, Billy Mark ; S/I ; 160241
dc.information.autorucEscuela de ingeniería ; Lillo Valles, Iván Alberto ; S/I ; 17890
dc.information.autorucEscuela de ingeniería ; Espinace Ronda, Pablo ; S/I ; 3892
dc.information.autorucEscuela de ingeniería ; Wendt González, Bruno Nicolás ; S/I ; 193988
dc.information.autorucEscuela de ingeniería ; Mery Quiroz, Domingo Arturo ; S/I ; 102382
dc.information.autorucEscuela de ingeniería ; Soto Arriaza, Álvaro ; S/I ; 73678
dc.language.isoen
dc.nota.accesoContenido parcial
dc.publisherIEEE
dc.relation.ispartofIEEE Winter Conference on Applications of Computer Vision (2015 : Waikoloa, HI, Estados Unidos)
dc.rightsacceso restringido
dc.subjectCameras
dc.subjectVisualization
dc.subjectTrajectory
dc.subjectTarget tracking
dc.subjectDetectors
dc.subjectTraining
dc.subjectDictionaries
dc.titleVisual Recognition to Access and Analyze People Density and Flow Patterns in Indoor Environmentses_ES
dc.typecomunicación de congreso
sipa.codpersvinculados12357
sipa.codpersvinculados169967
sipa.codpersvinculados160241
sipa.codpersvinculados17890
sipa.codpersvinculados3892
sipa.codpersvinculados193988
sipa.codpersvinculados102382
sipa.codpersvinculados73678
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