Vision-based Edge-line Assisted Moving Vehicle Detection

The detection of vehicles has a very wide range of applications in public transportation monitoring and management. Especially the moving vehicles detection plays an indispensable role in modern Intelligent Transport System (ITS). A reliable mobile vehicle detection method can provide necessary guarantees for traffic safety, assist drivers or pedestrians to predict road conditions, such as traffic statistics in designated areas, driver blind spot assistance, thereby ensuring the safety of pedestrians, passengers and drivers. Recently, most of the deep learning research stays on object detection and classification in the image, few studies on moving object detection in streaming video. In this paper, a low-cost vision-based moving vehicle detection method in streaming video is introduced. In order to distinguish between moving vehicles and stationary vehicles, a robust lane line detection method is used to detect the lanes, thereby avoiding the interference of stationary targets and background factors on the outside of the lanes. Then select the pixels in the road area and fill the cut area with pure white pixels, pass this reorganized data matrix to a pre-trained vehicles detection deep neural network to get the moving vehicles information. Experimental results show that this method can quickly and accurately identify moving vehicles on the road under a fixed viewing angle.