Algorithms for Vehicle Classification: Phase II
Robert Martin, Osama Masoud, Surendra Gupte, Nikolaos P Papanikolopoulos
Report no. Mn/DOT 2002-21
Topics: Traffic Modeling and Data
This report summarizes the research behind a real-time system for vehicle detection and classification in images of traffic obtained by a stationary CCD camera. The system models vehicles as rectangular bodies with appropriate dynamic behavior and processes images on three levels: raw image, blob, and vehicle. Correspondence is calculated between the processing levels as the vehicles move through the scene. This report also presents a new calibration algorithm for the camera. Implemented on a dual Pentium PC equipped with a Matrox Genesis C80 video processing board, the system performed detection and classification at a frame rate of 15 frames per second. Detection accuracy approached 95 percent, and classification of those detected vehicles neared 65 percent. The report includes an analysis of scenes from highway traffic to demonstrate this application.