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dc.contributor.authorYebes Torres, José Javier 
dc.contributor.authorBergasa Pascual, Luis Miguel 
dc.contributor.authorGarcía Garrido, Miguel Ángel 
dc.date.accessioned2020-06-10T17:40:02Z
dc.date.available2020-06-10T17:40:02Z
dc.date.issued2015-04-20
dc.identifier.bibliographicCitationYebes J.J, Bergasa L.M, & García-Garrido M.A. 2015, "Visual Object Recognition with 3D-Aware Features in KITTI Urban Scenes", Sensors. 2015, 15(4), 9228-9250
dc.identifier.issn1424-8220
dc.identifier.urihttp://hdl.handle.net/10017/43131
dc.description.abstractDriver assistance systems and autonomous robotics rely on the deployment of several sensors for environment perception. Compared to LiDAR systems, the inexpensive vision sensors can capture the 3D scene as perceived by a driver in terms of appearance and depth cues. Indeed, providing 3D image understanding capabilities to vehicles is an essential target in order to infer scene semantics in urban environments. One of the challenges that arises from the navigation task in naturalistic urban scenarios is the detection of road participants (e.g., cyclists, pedestrians and vehicles). In this regard, this paper tackles the detection and orientation estimation of cars, pedestrians and cyclists, employing the challenging and naturalistic KITTI images. This work proposes 3D-aware features computed from stereo color images in order to capture the appearance and depth peculiarities of the objects in road scenes. The successful part-based object detector, known as DPM, is extended to learn richer models from the 2.5D data (color and disparity), while also carrying out a detailed analysis of the training pipeline. A large set of experiments evaluate the proposals, and the best performing approach is ranked on the KITTI website. Indeed, this is the first work that reports results with stereo data for the KITTI object challenge, achieving increased detection ratios for the classes car and cyclist compared to a baseline DPM.en
dc.description.sponsorshipMinisterio de Educación, Cultura y Deportees_ES
dc.description.sponsorshipComunidad de Madrides_ES
dc.description.sponsorshipMinisterio de Economía y Competitividades_ES
dc.format.mimetypeapplication/pdfen
dc.language.isoengen
dc.publisherMDPI
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subject3D-aware featuresen
dc.subjectObject recognitionen
dc.subjectKITTIen
dc.subjectDPMen
dc.subjectStereo-visionen
dc.titleVisual object recognition with 3D-aware features in the urban scenes of KITTI evaluation benchmarken
dc.typeinfo:eu-repo/semantics/articleen
dc.subject.ecienciaElectrónicaes_ES
dc.subject.ecienciaElectronicsen
dc.contributor.affiliationUniversidad de Alcalá. Departamento de Electrónicaes_ES
dc.relation.publisherversionhttps://doi.org/10.3390/s150409228
dc.type.versioninfo:eu-repo/semantics/publishedVersionen
dc.identifier.doi10.3390/s150409228
dc.relation.projectIDAP2010-1472 (Ministerio de Educación, Cultura y Deporte)es_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/Comunidad de Madrid//S2013%2FMIT-2748/ES/Robótica aplicada a la mejora de la calidad de vida de los ciudadanos, fase III/RoboCity2030-III-CMen
dc.relation.projectIDinfo:eu-repo/grantAgreement/MINECO//TEC2012-37104/ES/SMART DRIVING APPLICATIONS/en
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen
dc.identifier.publicationtitleSensors


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