Thermal Imaging-Based Vehicle Classification in Nighttime Traffic

This research proposes a novel method for classifying vehicles in nighttime traffic by utilizing thermal imaging-based as the source of analysis. Using the proposed classifying method, light occlusions from vehicles and surrounding environment are able to be eliminated. Naturally, various thermal features from a vehicle, comprising windscreen, engine heat are spontaneously occurred and illustrate as different intensities in thermal images. In the research, the mentioned features are utilized for categorizing vehicle types. For classifying steps, initially, the proposed model will search for engine heat of a vehicle and select a suspected area which cover windscreen feature of the vehicle. Secondly, the area is thresholded for categorizing the intensity of windscreen, engine and environment heat, respectively. As the result, the vehicle front is able to be extracted from the mentioned area. For the classification which is the final step, the extracted vehicle front will be scaled, reformed and compared to assigned templates in the database for categorizing the type of vehicle. Experimentally, with the four types of vehicle, consisting of motorcycle, car, van and truck, respectively, the accuracy of classifications are over 86 %.