RESEARCH FOR TRAFFIC CENSUS USING SEGMENTATION OF AUTOMOBILE PARTS

Abstract

 In Japan, road administrators perform traffic censuses to understand the status of automobile traffic. In this census, it is common for investigators to visually check the automobiles and count the numbers of passing automobiles for both small and large cars. But, it is difficult to secure workers because the working age population is decreasing. Therefore, the Ministry of Land, Infrastructure, Transport and Tourism will abolish the manual survey and consider introducing some techniques that automatically count the number of passing automobiles using video images. In existing research, techniques for the classification of automobile type have been developed using machine learning or deep learning. These techniques are not yet accurate enough to be used in practice. In this research, we develop a technique to count the number of passing automobiles for each automobile type. This technique classifies the automobile type based on the outer shape of the automobile and the shape of the parts, which investigaters pay attention to when they classify the automobile type manually. Furthermore, we clarified the usefulness of the proposed technique through some demonstration experiments.