A single target voting scheme for traffic sign detection

Traffic sign detection and recognition is an important part of advanced driver assistance systems. Many prototype solutions for this task have been developed, and first commercial systems have just become available. Their image processing chain can be divided into three steps, preprocessing, detection, and recognition. Albeit several reliable sign recognition algorithms exist by now sign detection under real-world conditions is still unstable. Therefore, we address the first two steps of the processing chain presenting an analysis of widely used detectors, namely Hough-like methods. We evaluate several preprocessing steps and tweaks to increase their performance. Hence, the detectors are applied to a large, publicly available set of images from real-life traffic scenes. As main result we establish a new probabilistic measure for traffic sign colour detection and, based on the findings in our analysis, propose a novel Hough-like algorithm for detecting circular and triangular shapes. These improvements significantly increased detection performance in our experiments.