Three ways of using stereo vision for traffic light recognition

This paper introduces three methods to improve traffic light recognition by using the stereo camera color image in tandem with the disparity image. The first method is object candidate filtering by analyzing the disparity values inside an object candidate. The second method applies the relative positioning filter. Using the depth measurement obtained from the disparity image as well as the intrinsic and extrinsic calibration, a three dimensional distance from an object to the vehicle can be calculated. Based on known real world traffic light locations, the filter is able to suppress thirty to seventy percent of false positives while only decreasing the detection rate by one percent. This result shows the huge potential for range filtering in traffic light recognition in general. The third method is the hypothesis size enhancement when re-projecting a hypothesis into the image. This process is enabled by a real world traffic light model in conjunction with the depth measurement. It is shown that all true positive hypotheses will benefit from this technique, resulting in a massively better overlap with the ground truth labels. When evaluated frame wise, re-projection can improve detection rate by up to fifteen percent. This work primarily enhances traffic light hypotheses obtained by a baseline detector and thus requires a hypothesis disparity value. As a further contribution this paper presents different methods for determining a hypothesis-wide disparity and evaluates the differences in quality and quantity.