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Visual feature tracking based on PHD filter for vehicle detection

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dc.affiliation.dptoUC3M. Departamento de Ingeniería de Sistemas y Automáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Laboratorio de Sistemas Inteligenteses
dc.contributor.authorGarcía, Fernando
dc.contributor.authorPrioletti, Antonio
dc.contributor.authorCerri, Pietro
dc.contributor.authorBroggi, Alberto
dc.contributor.authorEscalera Hueso, Arturo de la
dc.contributor.authorArmingol Moreno, José María
dc.date.accessioned2016-10-03T10:57:13Z
dc.date.available2016-10-03T10:57:13Z
dc.date.issued2014-10-07
dc.description.abstractVehicle detection is one of the classical application among the Advance Driver Assistance Systems (ADAS). Applications like emergency braking or adaptive cruise control (ACC) require accurate and reliable vehicle detection. In latest years the improvements in vision detection have lead to the introduction of computer vision to detect vehicles by means of these more economical sensors, with high reliability. In the present paper, a novel algorithm for vehicle detection and tracking based on a probability hypothesis density (PHD) filter is presented. The first detection is based on a fast machine learning algorithm (Adaboost) and Haar-Like features. Later, the tracking is performed, by means features detected within the bounding box provided by the vehicle detection. The features, are tracked by a PHD filter. The results of the features being tracked are combined together in the last step, based on several different methods. Test provided show the performance of the PHD filter in public sequences using the different methods proposed.en
dc.description.sponsorshipThis work was supported by the Spanish Government through the Cicyt projects (GRANT TRA2010-20225-C03-01) and (GRANT TRA 2011-29454-C03-02).en
dc.format.extent7
dc.format.mimetypeapplication/pdf
dc.identifier.bibliographicCitationInformation Fusion (FUSION), 2014 17th International Conference on. IEEE, pp. 1-6en
dc.identifier.isbn978-8-4901-2355-3
dc.identifier.publicationfirstpage1
dc.identifier.publicationlastpage6
dc.identifier.publicationtitleInformation Fusion (FUSION), 2014 17th International Conference onen
dc.identifier.urihttps://hdl.handle.net/10016/23670
dc.identifier.uxxiCC/0000023263
dc.language.isoeng
dc.publisherIEEE
dc.relation.eventdate7-10 July 2014
dc.relation.eventplaceSalamanca, Castilla y León, Spainen
dc.relation.eventtitle17th International Conference on Information Fusionen
dc.relation.projectIDGobierno de España. TRA2010-20225-C03-01
dc.relation.projectIDGobierno de España. TRA 2011-29454-C03-02
dc.relation.projectIDGobierno de España. TRA2013-48314-C3-1-R
dc.rights© 2014 IEEE
dc.rights.accessRightsopen access
dc.subject.ecienciaRobótica e Informática Industriales
dc.subject.otherPHD Filteren
dc.subject.otherVehicle detectionen
dc.subject.otherComputer visionen
dc.subject.otherIntelligent Transport Systemsen
dc.titleVisual feature tracking based on PHD filter for vehicle detectionen
dc.typeconference paper*
dc.type.hasVersionAM*
dspace.entity.typePublication
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