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dc.contributor.authorThurnhofer-Hemsi, Karl
dc.contributor.authorLópez-Rubio, Ezequiel 
dc.contributor.authorDomínguez, Enrique
dc.contributor.authorLuque-Baena, Rafael Marcos 
dc.contributor.authorMolina-Cabello, Miguel Ángel 
dc.date.accessioned2017-05-29T12:36:18Z
dc.date.available2017-05-29T12:36:18Z
dc.date.created2017
dc.date.issued2017-05-29
dc.identifier.urihttp://hdl.handle.net/10630/13761
dc.description.abstractThe construction of a model of the background of a scene still remains as a challenging task in video surveillance systems, in particular for moving cameras. This work presents a novel approach for constructing a panoramic background model based on competitive learning neural networks and a subsequent piecewise linear interpolation by Delaunay triangulation. The approach can handle arbitrary camera directions and zooms for a Pan-Tilt-Zoom (PTZ) camera-based surveillance system. After testing the proposed approach on several indoor sequences, the results demonstrate that the proposed method is effective and suitable to use for real-time video surveillance applications.es_ES
dc.description.sponsorshipUniversidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.es_ES
dc.language.isoenges_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.subjectTeledetecciónes_ES
dc.subject.othercomputer visiones_ES
dc.subject.otherPTZ camerases_ES
dc.titlePanoramic Background Modeling for PTZ Cameras with Competitive Learning Neural Networkses_ES
dc.typeinfo:eu-repo/semantics/conferenceObjectes_ES
dc.centroE.T.S.I. Informáticaes_ES
dc.relation.eventtitleInternational Joint Conference on Neural Networks 2017es_ES
dc.relation.eventplaceAnchorage, Alaska, Estados Unidoses_ES
dc.relation.eventdateMayo 2017es_ES
dc.cclicenseby-nc-ndes_ES


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