D. Gerónimo, A.D. Sappa, A. López, and D. Ponsa (Spain)
ADAS, pedestrian detection, adaboost learning, pitch esti mation, haar wavelets, edge orientation histograms.
In this paper we propose a combination of different Haar filter sets and Edge Orientation Histograms (EOH) in or der 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 consist ing of discriminant features, selected by AdaBoost, is ap plied at pedestrian-sized image windows in order to per form the classification. Additionally, taking into account the final application, a driver assistance system with real time requirements, we propose a novel stereo-based cam era pitch estimation to reduce the number of explored win dows. With this approach, the system can work in urban roads, as will be illustrated by current results.
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