Convolutional Neural Network Based Traffic Sign Recognition System for Simultaneous Classification of Static and Dynamic Images

Traffic symbols are crucial part of the road infrastructure which are erected at the side of the roads that communicates basic instructions of the road with the help of simple visual graphics which can be understood in no time. As compared to previous decades, traffic congestion is a major issue faced in densely populated cities. Unfortunately, drivers may not notice these traffic signs due to adverse traffic conditions or ignorance which may cause accidents. Therefore, building an intelligent traffic sign recognition model is the need of the time. Besides contributing to the safety and comfort of drivers, traffic symbols recognition has important benefits for autonomous vehicles. In this paper, we have used simple CNN technique to recognize static as well as dynamic traffic symbols on the German Traffic Symbol Recognition Benchmark (GTSRB) dataset which has more than 40 classes of traffic symbols in different orientation and lightning condition. Further, a comparison of performance of CNN on static and dynamic input was done and the efficiency was compared. The experimental results show that the detection rate of the CNN model on static images is 96.15% which significantly higher than that of on dynamic images, which resulted in 95% accurate.