Traffic sign recognition system for autonomous vehicle using cascade SVM classifier

In past two decades, developing a system that can navigate vehicle autonomously becomes more interesting problem. The vehicle is equipped by sensors, such as radar, laser, GPS, and camera for sensing the surrounding. Among them, utilization of camera with computer vision technique is the most adopted method for constructing such a system. It is because camera provides a lot of information and is low-cost device rather than other sensors. Traffic road sign, as one of the important information from camera, carries a lot of useful information that are required for navigating. Thus, in this work, traffic sign detection and recognition is addressed. First, the input image is converted into normalize red and blue color space, as traffic sign usually appear with red and blue color. Second, maximally extremal stable region is then performed for extracting candidate region. Using heuristic rule of geometry properties, the false region will be excluded. Third, histogram of oriented gradient method is applied in order to extract feature from candidate region. Lastly, cascade support vector machine classifier is then processed to classify region belong to certain class of traffic sign. The extensive experiment would be carried out over German traffic sign recognition database and video. The experimental results demonstrate the effectiveness of our systems.