Reliable Traffic Sign Recognition System

Abstract

Traffic sign detection and recognition is an important part of Advance Driving Assistance Systems (ADAS), which aims to provide assistance to the driver, autonomous driving, or even monitoring of traffic signs for maintenance. Particularly, misclassification of traffic signs may have severe negative impact on safety of drivers, infrastructures, and human in the surrounding environment. In addition to shape and colors, there are many challenges to recognize traffic signs correctly such as occlusion, motion blur, visual camera’s failures, or physically altering the integrity of traffic signs. In Literature, different machine learning based classifiers and deep classifiers are utilized for Traffic Sign Recognition (TSR), with a few studies consider sequences of frames to commit final decision about traffic signs. This paper proposes a robust TSR against different attacks/failures such as camera related failures, occlusion, broken signs, and patches inserted on traffic signs. We are planning to utilize generative adversarial networks to corrupt images of traffic signs and investigate the robustness of TSR. Furthermore, we are currently working on designing a failure detector, which will help the TSR in advance before recognition, whether images are corrupted with some type of failure. Our conjecture is that failure detector with classifiers will improve the robustness of TSR system.