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dc.contributor.authorGarcía-González, Jorge
dc.contributor.authorOrtiz-de-Lazcano-Lobato, Juan Miguel 
dc.contributor.authorLuque-Baena, Rafael Marcos 
dc.contributor.authorMolina-Cabello, Miguel Ángel 
dc.contributor.authorLópez-Rubio, Ezequiel 
dc.date.accessioned2018-11-05T10:28:39Z
dc.date.available2018-11-05T10:28:39Z
dc.date.issued2018-11-05
dc.identifier.urihttps://hdl.handle.net/10630/16784
dc.description.abstractNowadays, the analysis and extraction of relevant information in visual data flows is of paramount importance. These images sequences can last for hours, which implies that the model must adapt to all kinds of circumstances so that the performance of the system does not decay over time. In this paper we propose a methodology for background modeling and foreground detection, whose main characteristic is its robustness against stationary noise. Thus, stacked denoising autoencoders are applied to generate a set of robust characteristics for each region or patch of the image, which will be the input of a probabilistic model to determine if that region is background or foreground. The evaluation of a set of heterogeneous sequences results in that, although our proposal is similar to the classical methods existing in the literature, the inclusion of noise in these sequences causes drastic performance drops in the competing methods, while in our case the performance stays or falls slightly.en_US
dc.description.sponsorshipUniversidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.en_US
dc.language.isospaen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectInteligencia artificialen_US
dc.subject.otherBackground modelingen_US
dc.subject.otherDeep learningen_US
dc.subject.otherAutoencodersen_US
dc.titleBackground modeling for video sequences by stacked denoising autoencodersen_US
dc.typeinfo:eu-repo/semantics/conferenceObjecten_US
dc.centroE.T.S.I. Informáticaen_US
dc.relation.eventtitleXVIII Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA)en_US
dc.relation.eventplaceGranada, Españaen_US
dc.relation.eventdate23/10/2018en_US


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