Feature detectors for traffic light recognition

Traffic light recognition belongs to the most difficult topics in the context of autonomous driving. Most described systems in literature follow a classical object recognition approach consisting of detection, verification and tracking. Proceeding from these three tasks, the detection part is of crucial importance, as overlooked traffic lights can most likely not be recovered in subsequent steps. Many published systems rely on feature detectors which try to detect a traffic lights lamp. The most frequently used feature detectors include the spotlight detector, color based detectors, and the circle detector. In contrast to other recognition tasks, no standard evaluation dataset exists to date. Additionally, only advanced systems consisting of detectors, verifiers and often tracking algorithms have been described in literature. In this paper, we introduce a dataset on which a fair comparison of traffic light feature detectors is made. We evaluate the effectiveness of the mentioned detectors and call attention to strengths and weaknesses of each detector.