Road Traffic Sign Detection and Classification.
Escalera, A., Moreno, L., et al.
A vision-based vehicle guidance system for road vehicles can have three main roles: (1) road detection; (2) obstacle detection; and (3) sign recognition. The first two have been studied for many years and with many good results, but traffic sign recognition is a less-studied field. Traffic signs provide drivers with very valuable information about the road, in order to make driving safer and easier. The authors think that traffic signs most play the same role for autonomous vehicles. They are designed to be easily recognized by human drivers mainly because their color and shapes are very different from natural environments. The algorithm described in this paper takes advantage of these features. It has two main parts. The first one, for the detection, uses color thresholding to segment the image and shape analysis to detect the signs. The second one, for the classification, uses a neural network.
Detection of Highway Warning Signs in Natural Video Images Using Color Image Processing and Neural Networks.
Kellmeyer, D. L., Zwahlen, H. T.
This study reports on the development of a system that incorporates color image processing and neural networks to detect and locate highway warning signs in natural roadway images. Such a system could reduce the need for redundant or oversized signs by assisting drivers in acquiring roadway information. Transportation agencies could use such a system as the first step in an automated highway sign inventory system. Currently, a human operator must watch hours of highway videos to complete this inventory. While only warning signs were considered in this study, the procedure was designed to be easily adapted to all highway signs. The basic approach is to digitize a roadway image and segment this image, using a back-propagation neural network, into eight colors that are important to highway sign detection. Next, the system scans the image for color...