No abstract

Abstract-This paper describes a method for classifying road signs based on a single color camera mounted on a moving vehicle. The main focus will be on the final neural network based classification stage of the candidates provided by an existing traffic sign detection algorithm. Great attention is paid to image preprocessing in order to provide a more simple and clear input to the network: candidate color images are cropped and converted to greyscale, then enhanced using a contrast stretching technique; a multi-layer perceptron neural network is then used to provide a matching score with different road sign models. Finally results are filtered using tracking. Benchmarks are presented, showing that the system is able to classify more then 200 different Italian road sign in real-time, with a recognition rate of 80% to 90%.

No abstract

Abstract-During the last few years many Advanced Driver Assistant Systems have been developed and a larger number of new car models every year is going to be equipped with these systems. However the product/function scenario lacks of common evaluation methodologies and tools for testing and improving performances of these systems. In this paper a validation methodology and a tool for Traffic Sign Recognition Systems evaluation (TSRs) is described.

Abstract-Color has proved to be an important feature to be exploited for road signs detection on images; however, not all road signs have distinctive color characteristics. This paper presents a shape-based approach for Italian de-restriction signs detection; the developed algorithm uses a black band extractor to highlight regions of interest, where a circle shape detection is performed. Tracking is used in order to increase reliability. The obtained detector is robust to different illumination conditions and shadows, and can manage different kinds of noise and perturbation. Despite its sensitiveness, the detector showed few false positives during performed tests.