A vision-based nighttime surrounding vehicle detection system

This study proposes a vision-based nightime surrounding nighttime vehicle detection system implemented by automatic multilevel thresholding method, denoise, connected component labeling, head light matching method, and head light tracking and classification. It is able to detect head light position, and get the vehicle position. Testing by four videos that we have record while we were driving, for side view, accuracy are all higher than 90%, and the time cost are all higher than 140 frames per second; for rear view, accuracy are all higher than 90%, and the time cost are all higher than 85 frames per second. By this system, we can make sure that there is any vehicle surrounding or not. If there is, it will decide which kind of vehicle it is, and notice the driver with different level, to supply more information and longer reaction time for driver, in order to decrease accident rate, increase driving safety.