| Summary: | 博士 === 國立交通大學 === 資訊科學與工程研究所 === 100 === With the rapid growth of surveillance equipments, detecting, tracking, and recognizing moving objects in roadway videos is currently a popular issue. Because most accidents are caused by cars and the appearance features of cars are hard to be hidden or counterfeited like license plate, this research develops an intelligent traffic monitoring system which identifies appearance features, such as sizes, models, and colors under varying camera viewpoint and light reflections. Due to the effect of non-homogeneous light reflection, the improper foreground pixels, such as the windshield, or lamps influence the extraction of color type. A tri-states car body segmentation algorithm is proposed in this dissertation. Different strategies are designed for bright, dark, and colored cars, and only the pixels belonging to the car body are considered for color classification. Therefore, a purer car color can be extracted and a more correct color type can be classified. To rapidly estimate the size and pose of a car, a symmetric center detection algorithm is proposed. The algorithm searches the symmetric center on the head (or rear) of a car and computes the distance between the center and the closest boundary as half of the head width. Two aspect ratios: car height to head (or rear) width and head (or rear) width to car width, are designed to identify the car size and car pose. To recognize car model across varying poses, a mirror morphing scheme is proposed. The scheme is able to transform cars with varying poses into a typical (front, rear, or side) view. Then a template car with the same pose with the tested car is selected and matched against the tested car. Because the mirror morphing scheme effectively reduces center bias and estimation error of tested and template cars, higher recognition rate can be anticipated. Finally, the experiments show that the proposed system is superior to conventional approaches for classifying colors, sizes, and models of cars.
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