A novel method for traffic sign recognition based on extreme learning machine

Abstract

As an important component of the driver assistance system or autonomous vehicle, traffic-sign recognition can provide drivers or vehicles with safety and alert information about the road. This paper proposes a new method for the task of traffic-sign recognition by employing extreme learning machine (ELM) whose infrastructure is a single-hidden-layer feed-forward network. This method includes two stages: One is the training stage which estimates the parameters of ELM based on training images of traffic signs; the other is the recognition stage which identifies each test image by using the trained ELM. Histogram-of-gradient descriptors are used as features in this proposed method. The German traffic sign recognition benchmark data set [1] with more than 50000 images of German road signs over 43 classes is used. Experimental results have shown that this proposed method achieves not only high recognition precision but also extremely low computational cost in terms of both training and recognition stages. An outstanding balance between recognition ratio and computational speed is obtained.