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dc.contributor.authorPedersoli, Marco-
dc.contributor.authorGonzàlez, Jordi-
dc.contributor.authorChakraborty, Bhaskar-
dc.contributor.authorVillanueva, Juan J.-
dc.identifier.citationComputer Recognition Systems 2: 739-746 (2007)-
dc.descriptionPresentado al CORES-2007 celebrado en Wroclaw (Poland).-
dc.description.abstractIn this paper we propose a human detection framework based on an enhanced version of Histogram of Oriented Gradients (HOG) features. These feature descriptors are computed with the help of a precalculated histogram of square-blocks. This novel method outperforms the integral of oriented histograms allowing the calculation of a single feature four times faster. Using Adaboost for HOG feature selection and Support Vector Machine as weak classifier, we build up a real-time human classifier with an excellent detection rate.-
dc.description.sponsorshipThis work was supported by the project 'Integration of robust perception, learning, and navigation systems in mobile robotics' (J-0929).-
dc.description.sponsorshipThis work is supported by EC grants IST-027110 for the HERMES project and IST-045547 for the VIDI -video project, and by the Spanish MEC under projects TIN2006-14606 and DPI-2004-5414. Jordi Gonzàlez also acknowledges the support of a Juan de la Cierva Postdoctoral fellowship from the Spanish MEC.-
dc.relation.ispartofseriesAdvances in Soft Computing 45-
dc.subjectPattern recognition: Computer vision-
dc.subjectComputer vision-
dc.titleEnhancing real-time human detection based on histograms of oriented gradients-
dc.typecomunicación de congreso-
dc.description.peerreviewedPeer Reviewed-
dc.contributor.funderMinisterio de Educación y Ciencia (España)-
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