Abstract
Detection of abnormal video images of transportation is to find out video images that contain abnormities among all images of transportation using video and image processing and analyzing techniques. It is an important component of intelligent transportation system, which can not only reduce the workload of traffic managers, but also effectively improve the efficiency of traffic management. However, video images of transportation in practice usually have complex backgrounds, and current detecting algorithms of traffic abnormity sometimes become ineffective due to interference factors such as noises and affine transformation (illumination variation, target occlusion, scale changes and view changes, etc.). In order to overcome these interference factors and fuzzy uncertainties in image representation, as well as improve the accuracy of traffic images representation, this study explored the representation methods of traffic images using fuzzy geometry theory on the basis of fuzzy uncertainties occurring during the process of imaging, transmission and processing of images; moreover, this study also put forward two kinds of representation algorithms of traffic images, and analyzed and verified effectiveness of representation algorithms based on theories and experiments.
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