Warning traffic sign recognition using a HOG-based K-d tree

Traffic signs are an essential part of a Driver Assistance System (DAS) and provide drivers with safety information. They are designed to be easily seen and understood. The triangular signs warn the drivers of imminent dangers such as wild animals or a sharp curve. In this paper, an efficient algorithm for the detection and recognition of warning signs is presented. A Histogram of Oriented Gradients (HOG) is used to detect 95% of the triangular warning signs. A blackhat filter eliminates a large part of the false alarms. An approximate nearest neighbors search using a KD-tree refines the result. It eliminates 100% of the remaining false detections and distinguishes amongst the different types of signs. The advantage of using HOG features is that all the warning signs, including static (red frame) and dynamic warning signs (illuminated) can be detected with a single detector and therefore, only one image scan.