Extending traffic light recognition: Efficient classification of phase and pictogram

While much work in the domain of traffic lights recognition is invested in the detection of traffic lights, classification of their exact state (including color phase and possible arrow pictogram) is often neglected. In this paper, we propose a robust approach for efficient video-based classification of said state with particular attention to the displayed pictogram and an additional ability to reject false detections. The currently active lights are identified and used to classify the phase. The lights are extracted and transformed into a HOG feature representation that is used to classify the pictogram with the help of machine learning classifiers. In order to gain optimal results, we compared the performance of different algorithms, namely LDA, kNN, and SVM. We provide an evaluation of our method on individual images and demonstrate that the classification rate of the phase lies at 96.7% and at 92.8% for the pictogram, with the use of SVMs providing best results. This leads to an overall classification quality of 89.9%. With a runtime of less than 1ms per image section our algorithm can easily be integrated in every traffic light recognition pipeline.