Traffic flow detection method based on vertical virtual road induction line

Abstract

Traffic flow detection is an important part of intelligent transportation system and it has a wide range of applications. We analyse the existing methods of traffic flow detection and propose a traffic flow detection method which based on vertical virtual road induction line (VVRIL). Firstly, according to the direction of the vehicle travelling, we set a VVRIL in the middle of the driveway. Secondly, the background image is gained from the video image with Gauss mixture model. We then make differential operation between the background image and video image to get a binary image, which we set the values of the foreground pixels as 1 and that of background pixels as 0. Thirdly, we extract the values of the pixels in the VVRIL of the binary image. Besides, we regard the vehicle maximum length obtained by self-learning as the length of the detection zone and get the information of vehicles in the VVRIL. Finally, we get the number of vehicles through the analysis of vehicle centre coordinates in the VVRIL of each video image. Experimental and theoretical analyses show that the method is accurate enough to meet the requirement of real-time performance.