Traffic sign recognition (TSR) represents an essential feature of advanced driver assistance systems (ADAS), by which a vehicle can recognize different traffic signs put on the road and thus contribute to the safety of the drivers, pedestrians, and cars. Computer vision techniques considered fundamental in pattern recognition are adopted for developing TSR systems. Though much research and work have been achieved, traffic sign recognition and detection are still very challenging if we want to provide a real-time processing system. SVM and CNN classifiers were used to train the model along with a Canadian traffic sign set to compare and predict the model.
Developing automated traffic sign recognition system helps and assist the driver to guarantee his/her life safety along with safety of other pedestrians as well. The main objective of the system is to detect and recognize traffic signs while driving. With the functionalities of the system, it can guide and alert the driver to prevent danger. Although the system can detect and recognize traffic sign, it doesn’t mean that each and every traffic signs can be correctly classified and recognized. Due to environmental challenges like lighting variation, bad weather, poor light illumination, rainfall, the system may not work correctly.