The Research On Speed Measurement Of Highway Vehicles Based On Machine Vision

The raise of the Internet of things (IOT) brings the intelligent traffic industry a brighter future prospect. The development of sensor technology, network technology, image processing and machine vision technology makes it possible for modern intelligent transportation systems to substitute human eye in the procedure of traffic monitoring. This paper studies the use of machine vision technology to monitor the scene on the highway, and the scene image analysis and processing speed of moving vehicles to extract the information technology, and attempted to provide the necessary traffic control data for the intelligent transportation systems (ITS).The key point of visually vehicle speed measurement lies in the way of abstracting the practical displacement of vehicles during the specific time scale promptly and accurately. Since there could be numbers of moving vehicles in the same circumstances, the author simplify the problem of the vehicle speed measurement into a key question of the measuring of moving vehicles distance, which include three key steps: vehicle target detection, target tracking and image positioning. Towards this problem the author obtain the conclusion by several steps as follows.(1) Established the accurate detection algorithm of the moving target. The authors compared three commonly used methods of motion detection and object detection based on background difference as a target detection method in this dissertation; application of Gaussian mixture model to adapt reconstruct and update the background and made a reasonable improvement. Taking the fog into account, this paper put forward an automatically Anti-Haze algorithm applicable to the high-way fog scene.(2) The author improved the target tracking algorithm being used in highway scenes. In the steps of tracking the direction of movement for the target scale and bandwidth of proposed changes in regulation strategy and goals down forward search strategy had been proposed, which made the target tracking algorithm adaptive to track fast moving targets in the specific scene on the highway.(3) Realized and improved the calibration orientated image localization method. Therefore proposed location method based on geometric measurements and full geometric positioning method. The author also proposed a hardware installation program with reliable feasibility which enriched the image positioning theory and the all-situation adaptability of the image localization.(4) In this dissertation, through simulation and field test, the proposed target detection, target tracking and image positioning algorithm had been implemented. According to the results of target detection and tracking of moving targets the author obtained the spatial location of each moment and the target trajectory in the monitoring period, combined with image positioning algorithm can the author obtain the real distance that the vehicle went through in the road scene. Calculating the real time of moving away from the scene, obtained the frame timing of the speed of vehicles. Through the frame timing method the author obtained the vehicle's moving time thus calculate the vehicle speed in the corresponding period. Finally a practical machine vision-based highway vehicle speed system design. Experimental results show that the proposed target detection method can effectively improve the detection accuracy by 13.9%, and the average error can be controlled within 2%, these results proved that machine vision-based highway vehicle velocity measurement theory is feasible, is able to meet the system requirements for the actual velocity measurement.