Vehicle classification based on images from visible light and thermal cameras

Abstract

We propose novel vehicle detection and classification methods based on images from visible light and thermal cameras. These methods can be used in real-time smart surveillance systems. To classify vehicles by type, we extract the headlight and grill areas from the visible light and thermal images. We then extract texture characteristics from the images and use these as features for classifying different types of moving vehicles. We also extract several features from images obtained at night and during the day, which are the contrast, homogeneity, entropy, and energy. We validated our method experimentally and achieved that the accuracy of our visible image classifier was 92.7% and the accuracy of our thermal image classifier was 65.8% when vehicles were classified into six types such as SUV type, sedan type, RV type.