Vehicle Detection and Classification Using Model-Based and Fuzzy Logic Approaches

Abstract

Vehicle detection and classification information is invaluable in many transportation issues. Vehicle feature extraction and detection are the preprocesses required for vehicle classification. Current automatic vehicle classification systems have deficiencies: low accuracy, special requirements, fixed orientation of the camera, or additional hardware and devices. This paper discusses a vehicle detection and classification system using model-based and fuzzy logic approaches. The system was tested with the use of a variety of images captured by the highway traffic control center of the Utah Department of Transportation. In comparison with existing systems, major advantages of the proposed system are (a) no special orientation of the camera is required, (b) no additional devices are needed, and (c) high classification accuracy is provided. Experimental results show that the performance of the proposed system exceeds that of the existing video-based vehicle classification systems.