Increasing accuracy of traffic light color detection and recognition using machine learning

Traffic light detection is an important system because it can alert driver on upcoming traffic light so that he/she can anticipate a head of time. In this paper we described our work on detecting traffic light color using machine learning approach. Using HSV color representation, our approach is to extract features based on an area of X×X pixels. Traffic light color model is then created by applying a learning algorithm on a set of examples of features representing pixels of traffic and non-traffic light colors. The learned model is then used to classify whether an area of pixels contains traffic light color or not. Evaluation of this approach reveals that it significantly improves the detection performance over the one based on value-range color segmentation technique.