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Joint Probabilistic Data Association fusion approach for pedestrian detection

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dc.affiliation.dptoUC3M. Departamento de Ingeniería de Sistemas y Automáticaes
dc.affiliation.dptoUC3M. Departamento de Informáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Laboratorio de Sistemas Inteligenteses
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Laboratorio de Control, Aprendizaje y Optimización de Sistemas (CAOS)es
dc.contributor.authorGarcía, Fernando
dc.contributor.authorEscalera Hueso, Arturo de la
dc.contributor.authorArmingol Moreno, José María
dc.date.accessioned2016-09-23T07:52:23Z
dc.date.available2016-09-23T07:52:23Z
dc.date.issued2013
dc.description.abstractAbstract: Fusion is becoming a classic topic in Intelligent Transport System (ITS) society. The lack of trustworthy sensors requires the combination of several devices to provide reliable detections. In this paper a novel approach, that takes advantage of the Joint Probabilistic Data Association technique (JPDA) for data association, is presented. The approach uses one of the most powerful techniques of Multiple Target Tracking theory and adapts it to fulfill the strong requirements of road safety applications. The different test performed proved that a powerful association technique can enhance the capacity of Advance Driver Assistance Systems. Two main sensors are used for pedestrian detection: laser scanner and computer vision. Furthermore, the approach takes advantage of the availability of other information sources i.e. context information and online information (GPS). The detections are fused using JPDA, enhancing the capacities of classical pedestrian detection systems, mainly based in visual information. The test performed also showed that JPDA improved the results offered by other data association techniques, e.g. Global Nearest Neighbors.en
dc.description.sponsorshipThis work was supported by the Spanish Government through the Cicyt projects (GRANT TRA2010-20225-C03- 01) and (GRANT TRA2011-29454-C03-02). CAM through SEGAUTO-II ( S2009/DPI-1509) .en
dc.format.extent7
dc.format.mimetypeapplication/pdf
dc.identifier.bibliographicCitationIntelligent Vehicles Symposium (IV), 2013 IEEE. : Ieee - The Institute Of Electrical And Electronics Engineers, Inc. Pp. 1344-1349
dc.identifier.isbn978-1-4673-2754-1
dc.identifier.publicationfirstpage1344
dc.identifier.publicationlastpage1349
dc.identifier.publicationtitleIntelligent Vehicles Symposium (IV), 2013 IEEEen
dc.identifier.urihttps://hdl.handle.net/10016/23622
dc.identifier.uxxiCC/0000021789
dc.language.isoeng
dc.publisherIeee - The Institute Of Electrical And Electronics Engineers, Incen
dc.relation.eventdate23-26 June 2013
dc.relation.eventplaceGold Coast City, Australiaen
dc.relation.eventtitle2013 IEEE Intelligent Vehicles Symposium (IV)
dc.relation.projectIDGobierno de España. TRA2010-20225-C03-01
dc.relation.projectIDGobierno de España. TRA2011-29454-C03-02
dc.relation.projectIDComunidad de Madrid. S2009/DPI-1509
dc.relation.publisherversionhttp://dx.doi.org/10.1109/IVS.2013.6629653
dc.rights© 2013 IEEE
dc.rights.accessRightsopen access
dc.subject.ecienciaRobótica e Informática Industriales
dc.subject.otherLaser fusionen
dc.subject.otherSensorsen
dc.subject.otherCamerasen
dc.subject.otherVehiclesen
dc.subject.otherJointsen
dc.subject.otherLaser modesen
dc.titleJoint Probabilistic Data Association fusion approach for pedestrian detectionen
dc.typeconference paper*
dc.type.hasVersionAM*
dspace.entity.typePublication
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