Computer Vision and Machine Learning techniques contribute to a wide range of interesting applications. A well known application among them is road sign detection and classification, which is a very active field of research. Although the first works in this area date back to the 1960s, significant progress has been made during the last years. Automatic road sign detection and classification has many practical implementations. The most widespread is the usage of the method in order to create

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... er Assistance Systems (DAS). Such systems aspire to the fully automatic navigation of the car through auto pilot and include systems for automatic lane detection, obstacle detection in the vehicle path and road sign recognition. For the time being, road sing detection and classification shall be used in order to assist the driver and enhance safety, e.g., by sending warning signals revealing over speed or indicating the presence of a specific sign that the driver may not notice due to distraction and lack of attention. Another interesting application is the mapping of the traffic signs, in order to be used for automated road maintenance. For this purpose, the system developed shall be used in combination with a mobile mapping system, offering information about the exact location of each detected sign. Traffic sign recognition systems have to face many challenges. First of all, illumination conditions are not controllable. Depending on the time of the day and the weather conditions, illumination may vary dramatically. Secondly, traffic signs may be partially damaged, vandalized or with faded color due to long exposure to sunlight; problems that hamper the successful detection and recognition. Other common problems are occlusions and shadows occurring by other objects surrounding the traffic signs as well as the existence of similar objects, which may be detected as road signs. Finally, possible rotations and translation of the traffic signs occur, thus the system developed should be invariant under rotation and translation. The thesis is organized as follows. Firstly, the state of the art methods for traffic sign detection and classification are described. Afterwards, the proposed the system, including the theoretical background of the techniques applied is presented and v analysed.Τhe proposed system uses color-images acquired by a low cost camera mounted on a moving vehicle. Color based detection is used in order to locate regions of interest. Then, a circular Hough transform is applied to complete detection, taking advantage of the shape properties of the road signs. The regions of interest are finally represented using HOG descriptors and are fed into trained Support Vector Machines (SVMs) in order to be recognized. For the training procedure, a database with several training examples depicting Greek road sings has been developed. Many experiments have been conducted and are presented, to measure the efficiency of the proposed methodology especially under adverse weather conditions and poor illumination. The final results are presented and discussed and the conclusions of the thesis are presented, along with the cited references.