Traffic Sign Recognition

Traffic Sign Recognition (TSR) from video images is an integral part of the driver support functions needed to make intelligent vehicles a reality. TSR is composed of two components: detection and classification. The focus of the proposed research is detection of U.S traffic signs based on the LISA dataset, the largest publicly available U.S traffic sign dataset in the world, comprising over 9,000 images. Detection methods involving Integral Channel Features and Aggregate Channel features have achieved state-of-the art performance. Our proposed research consists of a comparative study of the performance of Integral Channel Features and Aggregate Channel features versus using Convolutional Neural Networks (CNN). Our aim is to explore the detection performance of the CNN by varying the convolutional layers, max-pooling layers, and the fully-connected layers. We will evaluate our detection performance by using the PASCAL measure, which is a standard metric for this application.