Intelligent Transportation System (ITS) is a system that builds a safe, effective and integrated transportation environment based on advanced technologies. Road signs detection and recognition is an important part of ITS, which offer ways to collect the real time traffic data for processing at a central facility.This project is to implement a road sign recognition model based on AI and image analysis technologies, which applies a machine learning method, Support Vector Machines, to recognize road signs. We focus on recognizing seven categories of road sign shapes and five categories of speed limit signs. Two kinds of features, binary image and Zernike moments, are used for representing the data to the SVM for training and test. We compared and analyzed the performances of SVM recognition model using different features and different kernels. Moreover, the performances using different recognition models, SVM and Fuzzy ARTMAP, are observed.

This thesis is focused on developing a robust algorithm for recognition of road signs including all stages of a machine vision system i.e. image acquisition, pre-processing, colour segmentation, labelling and classifi-cation. Images are acquired by two different imaging systems and noise removal is done by applying Mean filter. Furthermore, different colour segmentation methods are investigated to find out the most high-performance approach and after applying dynamic segmentation based on blue channel in YCbCr colour space, the obtained binary image is transferred to a personal computer through the developed PC software using standard serial port and further processing and classification is run on the PC. Histogram of Oriented Gradients (HOG) is used as the main feature for recognition of road signs and finally the classification task is fulfilled by employing hardware efficient Minimum Distance Classifier (MDC).

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In this thesis, video frames acquired by a camera in a moving car are processed for detection of candidates of triangular, rectangular and circular traffic/road signs based on mainly shape information by performing contour analysis. Color information is utilized as an auxiliary method to improve detection. Then recognition based on template matching is realized on detected traffic/road sign candidates. Detection and recognition results of traffic/road signs in video frames taken in different time intervals of day for these methods are compared. After implementation, results show that the video scene taken in a sunny day in the afternoon gives better results than others. Binary threshold plays a great role in detection with respect to Canny edge detector especially for triangular and rectangular traffic signs. Higher number of binary threshold levels improves detection in general. In addition, the recognition rate for triangular and rectangular traffic/road signs is higher than that of circular sings in general by the methods used in this study.