RESEARCH APPLIYING GAN TO NIGHTTIME TRAFFIC CENSUS

Abstract

 In Japan, road administrators perform traffic censuses to understand the status of automobile traffic. Recently, in these censuses, techniques for counting the traffic volume from video images have attracted attention for the purpose of improving work efficiency and to save labor. These techniques can count with practical accuracy in video images taken in the daytime. However, the counting accuracy is reduced in video images taken in the nighttime as sufficient brightness are not secured and a shape and color of the vehicle are obscured. In this research, we develop a traffic census technique for application to nighttime traffic using existing techniques. This technique converts video images shooted the nighttime into video images taken in the daytime using deep learning. Furthermore, we clarified the usefulness of the proposed technique through a demonstration experiment.