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dc.contributor.authorMolina-Cabello, Miguel Ángel 
dc.contributor.authorLuque-Baena, Rafael Marcos 
dc.contributor.authorLópez-Rubio, Ezequiel 
dc.contributor.authorThurnhofer-Hemsi, Karl
dc.date.accessioned2017-07-05T10:54:50Z
dc.date.available2017-07-05T10:54:50Z
dc.date.issued2017
dc.identifier.citationJ.M. Ferrández Vicente et al. (Eds.): IWINAC 2017, Part II, LNCS 10338, pp. 268–278, 2017. DOI: 10.1007/978-3-319-59773-728es_ES
dc.identifier.urihttp://hdl.handle.net/10630/14114
dc.description.abstractIn this work a new vehicle type detection procedure for traffic surveillance videos is proposed. A Convolutional Neural Network is integrated into a vehicle tracking system in order to accomplish this task. Solutions for vehicle overlapping, differing vehicle sizes and poor spatial resolution are presented. The system is tested on well known benchmarks, and multiclass recognition performance results are reported. Our proposal is shown to attain good results over a wide range of difficult situations.es_ES
dc.description.sponsorshipUniversidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.es_ES
dc.language.isoenges_ES
dc.publisherSpringeres_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.subjectRedes neuronales (Informática)es_ES
dc.subject.otherForeground detectiones_ES
dc.subject.otherBackground modelinges_ES
dc.subject.otherConvolutional neural networkses_ES
dc.subject.otherProbabilistic self-organizing mapses_ES
dc.subject.otherBackground featureses_ES
dc.titleVehicle Type Detection by Convolutional Neural Networkses_ES
dc.typeinfo:eu-repo/semantics/conferenceObjectes_ES
dc.centroE.T.S.I. Informáticaes_ES
dc.relation.eventtitleInternational Work-Conference on the Interplay between Natural and Artificial Computation 2017es_ES
dc.relation.eventplaceLa Coruñaes_ES
dc.relation.eventdateJunio 2017es_ES
dc.identifier.orcidhttp://orcid.org/0000-0001-8231-5687es_ES
dc.cclicenseby-nc-ndes_ES


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