www.cvc.uab.es/adas Abstract. On–board pedestrian detection is a key task in advanced driver assistance systems. It involves dealing with aspect–changing ob-jects in cluttered environments, and working in a wide range of distances, and often relies on a classification step that labels image regions

sparse image sampling, as well as a classifier for pedestrian detection based on Haar wavelets and edge orientation histograms as features and AdaBoost as learning machine. Both proposals are compared with relevant approaches in the literature, showing comparable results but reducing processing time

the number of explored win-dows. With this approach, the system can work in urban roads, as will be illustrated by current results. KEY WORDS ADAS, pedestrian detection, adaboost learning, pitch esti-mation, haar wavelets, edge orientation histograms. 1

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April 29, 2011Für UnsShe had never before seen a rabbit with either a waistcoat-pocket, or a watch to take out of it, and burning with curiosity, she ran across the field after it Lewis CarrollThe following document, written under the supervision of Dr. reviewed by: