Pedestrian detection using adaboost learning of features and vehicle pitch estimation

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 perform the 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.