UC Riverside

Vehicle classification has become a very important subject of study because of its importance in autonomous navigation, surveillance and traffic analysis. Classification of vehicles from the rear view is challenging because all the vehicles have subtle appearance differences from the rear view, changing illumination conditions, presence of shadows and real-time considerations. While numerous approaches have been introduced for this purpose, no specific study has been conducted to provide a robust and complete video-based vehicle classification system based on the rear view where the camera’s field of view is directly behind the vehicle. In this paper we present a multi-class vehicle classification system which classifies a vehicle into one of four possible classes Sedan, Minivan, SUV and Pickup truck when seen from its rear view. For a given geometric setup of the camera we use a feature set of Visual Rear Ground Clearance, height of the vehicle and perpendicular distance between the bottom of the license plate and bottom of the rear bumper for classifying the vehicle. Results are shown on large data-sets of freeway videos.