Haar Wavelets and Edge Orientation Histograms for On---Board Pedestrian Detection

Authors:
David Gerónimo;Antonio López;Daniel Ponsa;Angel D. Sappa
Affiliations:
Computer Vision Center, Universitat Autònoma de Barcelona, Edifici O, 08193 Bellaterra, Barcelona, Spain;Computer Vision Center, Universitat Autònoma de Barcelona, Edifici O, 08193 Bellaterra, Barcelona, Spain;Computer Vision Center, Universitat Autònoma de Barcelona, Edifici O, 08193 Bellaterra, Barcelona, Spain;Computer Vision Center, Universitat Autònoma de Barcelona, Edifici O, 08193 Bellaterra, Barcelona, Spain
Venue:
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I
Year:
2007

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Abstract

On---board pedestrian detection is a key task in advanced driver assistance systems. It involves dealing with aspect---changing objects in cluttered environments, and working in a wide range of distances, and often relies on a classification step that labels image regions of interest as pedestrians or non---pedestrians. The performance of this classifier is a crucial issue since it represents the most important part of the detection system, thus building a good classifier in terms of false alarms, missdetection rate and processing time is decisive. In this paper, a pedestrian classifier based on Haar wavelets and edge orientation histograms (HW+EOH) with AdaBoost is compared with the current state---of---the---art best human---based classifier: support vector machines using histograms of oriented gradients (HOG). The results show that HW+EOH classifier achieves comparable false alarms/missdetections tradeoffs but at much lower processing time than HOG.