%0 Generic
%T Pedestrian Detection Using AdaBoost Learning of Features and Vehicle Pitch Estimation
%A David Geronimo
%A Angel Sappa
%A Antonio Lopez
%A Daniel Ponsa
%D 2006
%F David Geronimo2006
%O ADAS
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=672), last updated on Mon, 14 Oct 2013 15:54:18 +0200
%X In this paper we propose a combination of different Haar filter sets and Edge Orientation Histograms (EOH) in order to learn a model for pedestrian detection. As we will show, with the addition of EOH we obtain better ROCs than using Haar filters alone. Hence, a model consisting of discriminant features, selected by AdaBoost, is applied at pedestrian-sized image windows in order to performthe classification. Additionally, taking into account the final application, a driver assistance system with realtime requirements, we propose a novel stereo-based camera pitch estimation to reduce the number of explored windows.With this approach, the system can work in urban roads, as will be illustrated by current results.
%K ADAS
%K pedestrian detection
%K adaboost learning
%K pitch estimation
%K haar wavelets
%K edge orientation histograms.
%9 miscellaneous
%U http://www.cvc.uab.es/adas/publications/geronimo_viip2006.pdf
%U http://refbase.cvc.uab.es/files/GSL2006.pdf