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dc.contributor.authorVillamizar, Michael-
dc.contributor.authorGrabner, Helmut-
dc.contributor.authorMoreno-Noguer, Francesc-
dc.contributor.authorAndrade-Cetto, Juan-
dc.contributor.authorGool, Luc van-
dc.contributor.authorSanfeliu, Alberto-
dc.identifierdoi: 10.5244/C.25.20-
dc.identifier.citationProceedings of the British Machine Vision Conference: 20.1-20.10 (2011)-
dc.descriptionPresentado al 22nd BMVC celebrado en University of Dundee (Escocia) en septiembre de 2011.-
dc.description.abstractWe propose an efficient method for object localization and 3D pose estimation. A two-step approach is used. In the first step, a pose estimator is evaluated in the input images in order to estimate potential object locations and poses. These candidates are then validated, in the second step, by the corresponding pose-specific classifier. The result is a detection approach that avoids the inherent and expensive cost of testing the complete set of specific classifiers over the entire image. A further speedup is achieved by feature sharing. Features are computed only once and are then used for evaluating the pose estimator and all specific classifiers. The proposed method has been validated on two public datasets for the problem of detecting of cars under several views. The results show that the proposed approach yields high detection rates while keeping efficiency.-
dc.description.sponsorshipThis work was supported by the Spanish Ministry of Science and Innovation under Projects RobTaskCoop (DPI2010-17112), PAU (DPI2008-06022), and MIPRCV (Consolider - Ingenio 2010 CSD2007-00018), and the EU CEEDS Project FP7-ICT-2009-5-95682.-
dc.publisherBritish Machine Vision Association-
dc.relation.isversionofPublisher's version-
dc.titleEfficient 3D object detection using multiple pose-specific classifiers-
dc.typecomunicación de congreso-
dc.description.versionPeer Reviewed-
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