Spatial Feature Extractions to Reduce Intra-Class Variability in Traffic Sign Templates

This paper presents shape features and a normalization scheme to address intra-class variation challenge of various traffic sign template designs. Traffic sign design around the world must conform to certain guidelines. However, there may be spatial differences among traffic signs belonging to a same class. In this work, we propose 22 features to discriminate 23 classes of traffic signs. This low number of features would minimize the classification time. We highlight the usage of location and directional information of local shape features. The application of the features is explained using the Naïve Bayes classifier for classification of the images. A dataset containing traffic sign templates with up to a maximum of 22 countries per class was used in our experiment to simulate intra-class variations. The features were compared against twelve other classifiers. The Naive Bayes classification technique performs the best with 99.4% classification accuracy and average of 0.43ms classification time per feature set. We proved that the features proposed are effective to discriminate inter-class images even though there are intra-class differences.