Road Sign Detection from Edge Orientation Histograms
This paper presents a system for road sign detection based on edge orientation histograms. Edge orientation histograms are reliable, scale and contrast invariant features that can be extracted efficiently using integral images. A learning method is introduced that selects features based on the implicit transmission function of the designer's template to the object's appearance in the image. The system is able to detect 85% of the objects on from 12 pixels width and 95% for objects on from 24 pixels width at a low false alarm rate.