A novel evolving classifier with a false alarm class for speed limit sign recognition

Automatic interpretation of information written on roadside signs is very useful in applications for conducting road surveys, driving autonomous vehicles, and improving the road safety and infrastructure. However, it is a very difficult problem because of high similarity and poor visibility of signs and challenging nature of road environment. In this paper, we propose a novel technique for automatic detection and recognition of speed limit signs. The proposed technique contains two novel concepts. Firstly, pixel-wise segmentation of all speed limit signs into one class instead of each sign in a separate class is proposed. Secondly, a novel classifier with a false alarm class that evolves its weights so that it can distinguish speed signs from non-speed signs is proposed. The proposed technique is evaluated on a real-world dataset provided by our industry partners. The dataset has been created from videos of all state roads in Queensland. The comparative analysis of results showed that the proposed technique is able to detect and classify speed limit signs with high accuracy.