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IBPRIA
2007
Springer

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

8 years 11 months ago
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.
David Gerónimo, Antonio M. López, Da
Added 07 Jun 2010
Updated 07 Jun 2010
Type Conference
Year 2007
Where IBPRIA
Authors David Gerónimo, Antonio M. López, Daniel Ponsa, Angel Domingo Sappa
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