ROAD SEGMENTATION WITH FUZZY AND SHADOWED SETS

摘要

Automatic road segmentation plays an important role in many vision-based traffic applications, such as traffic surveillance, traffic flow measurement, traffic accident/incident detection, vehicle guidance, and driver assistance. Road segmentation provides useful information for precluding from further consideration the objects, events and activities appearing outside road areas. Therefore, the interference of irrelevant objects, activities and events can be avoided beforehand. In addition, the processing time can be saved. The proposed road segmentation method consists of four major steps: background image generation, foreground object extraction, background pasting, and road localization. In the first step, the background image of the scene is generated using a histogram-based progressive technique. The generated background image is used in the second step to fast extract foreground objects from each input video image. The background patches corresponding to the extracted foreground objects are pasted in an image, called the road image. Repeating the second and the third steps for each input image, a major component of the road region will gradually be constructed. To obtain the full road region, two more tasks need to be fulfilled; they are hole filling and road localization. The first task is accomplished by invoking a morphological process and the second task is achieved using a fuzzy-shadowed set theoretic technique. The experimental results have revealed that the proposed method can effectively detect the road area. Moreover, our method requires no a priori information about both camera setup and image scale.