Semi-supervised Training Set Adaption to Unknown Countries for Traffic Sign Classifiers

Abstract

Traffic signs in Western European countries share many similarities but also can vary in colour, size, and depicted symbols. Statistical pattern classification methods are used for the automatic recognition of traffic signs in state-of-the-art driver assistance systems. Training a classifier separately for each country requires a huge amount of training data labelled by human annotators. In order to reduce these efforts, a self-learning approach extends the recognition capability of an initial German classifier to other European countries. After the most informative samples have been selected by the confidence band method from a given pool of unlabelled traffic signs, the classifier assigns labels to them. Furthermore, the performance of the self-learning classifier is improved by incorporating synthetically generated samples into the self-learning process. The achieved classification rates are comparable to those of classifiers trained with fully labelled samples.