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dc.contributor.authorMolina-Cabello, Miguel A.
dc.contributor.authorLópez-Rubio, Ezequiel 
dc.contributor.authorLuque-Baena, Rafael Marcos
dc.contributor.authorDomínguez, Enrique
dc.contributor.authorThurnhofer-Hemsi, Karl
dc.date.accessioned2017-05-29T12:35:17Z
dc.date.available2017-05-29T12:35:17Z
dc.date.created2017
dc.date.issued2017-05-29
dc.identifier.urihttp://hdl.handle.net/10630/13760
dc.description.abstractAbstract—In this paper a controller for PTZ cameras based on an unsupervised neural network model is presented. It takes advantage of the foreground mask generated by a nonparametric foreground detection subsystem. Thus, our aim is to optimize the movements of the PTZ camera to attain the maximum coverage of the observed scene in presence of moving objects. A growing neural gas (GNG) is applied to enhance the representation of the foreground objects. Both qualitative and quantitative results are reported using several widely used datasets, which demonstrate the suitability of our approach.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.titleNeural Controller for PTZ cameras based on nonpanoramic foreground detectiones_ES
dc.typeinfo:eu-repo/semantics/preprintes_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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