Haar wavelets and edge orientation histograms for on-board pedestrian detection

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.