An adaptive system for traffic sign recognition

Traffic sign recognition is a primary goal of almost all road environment understanding systems. A vision system for traffic sign recognition was developed by Daimler-Benz Research Center Ulm. The two main modules of the system are detection and verification (recognition). Here regions of possible traffic signs in a color image sequence are first detected before each of them is verified and recognized. In this paper the authors pay attention to the verification and recognition process. The authors present an adaptive approach and emphasize the importance of the adaptability to various road and traffic sign environments. The authors utilize a distance-weighted k-nearest-neighbor classifier for traffic sign recognition and show its equivalence to the kind of radial basis function networks which can be easily integrated into chips. The authors also present a way to evaluate the uncertainty of recognized traffic signs and demonstrate their approach using real images.