Development of a Real-time Speed Limit Sign Recognition System

Abstract

An automatic speed-limit sign recognition system could be one important feature in a driver assistance system. This paper presents a real-time system to detect speed limit signs and remind drivers about the allowable speed limit in a specific road. The developed system consists of two main tasks, namely detection and recognition. In general, a speed limit sign is detected and extracted from real world scenes on the basis of their color and shape features. The detection task is based on a combination of color segmentation and shape detection techniques. It significantly speeds up the shape detection process by calculating the cross-correlation in the frequency domain. Next, classification is then performed on extracted candidate region using multi-layer perceptron neural networks. The experimental results proved the capability of the system with various sign sizes, backgrounds and lighting conditions.