Traffic Light Detection Based on Multi-feature Segmentation and Online Selecting Scheme

Wenhao Zong, Qijun Chen

Abstract: This paper is concerned with vision-based traffic light detection by using multi-feature to segment one single image and an online selecting scheme. First, we propose a new simple method called edged-color image to segment candidate traffic light back board regions from even complex background, which is a way to enhance edge information in a color image substantially. Second, an online selecting scheme is used to calculate whether two or more candidate regions can be combined together. Those with faulty score closer to zero will be regarded as a traffic light. In addition, arrow light will be recognized from the traffic light. Applying the method above can mostly solve the problems as different light intensity, complex background, vehicle tail light, etc.