End to End Deep Neural Network Classifier Design for Universal Sign Recognition

Vartika Sengar, Renu M. Rameshan, Senthil Ponkumar

Abstract: Self-driving cars and Advanced Driver Assistance Systems rely heavily on Traffic Sign Recognition for safe maneuvering on the roads. But traffic signs can vary from one country to another, thereby necessitating multiple classifiers or a single universal classifier which can handle variations across countries. This paper reports our attempt at building a universal classifier. This classifier has to deal with large intra-class variations in the classes and also similarities among various difficult to distinguish traffic sign classes. This paper is an extension of our previous work in which we proposed a hierarchical classifier for traffic signs of a specific country. In hierarchical classification, dedicated classifiers are trained for classes which are more difficult to distinguish. Such similar classes are grouped together automatically by learning category hierarchy from the confusion matrix of a flat classifier (building block). In this paper, we use attention network for country independent classification. Here, CNN itself pays attention to regions in an image which are more discriminative and thus results in better classification for such problems. The aim here is to design a traffic sign recognition framework which can be used for multiple countries and be able to classify even the hard to distinguish classes by exploiting category hierarchy of traffic signs. The model is evaluated on traffic signs of seven countries namely Belgium, China, Croatia, Russia, Spain, Germany and Italy. The new building block architecture shows significant improvement of classification accuracy that is 97.7% as compared to building block architecture (VGG) used in our previous paper that is 95.1%.