Traffic signs are characterized by a wide variability in their visual appearance in real-world environments. For example, changes of illumination, varying weather conditions, and partial occlusions impact the perception of road signs. In practice, a large number of different sign classes needsneed to be recognized with very high accuracy. Traffic signs have been designed to be easily readable for humans, who perform very well at this task. For computer systems, however, classifying traffic signs still seems to pose a challenging pattern recognition problem. Both image processing and machine learning algorithms are continuously refined to improve on this task.
The first part of this paper provides an overview of previous work on traffic sign recognition. First achievements starting from the late 80s are presented. Various components are discussed, such as detection, classification, and temporal integration. The next point of thesethis thesis is history and introduction to neural networks. Systems using neural networks tofor classification are presented. State-of-the-art in this field, such as Faster R-CNN and YOLO v3 are discussed.