The Applied Research And Development Of Video Detection Technology In Urban Intersection Signal Adaptive Control

The development of economy speeds up the progress of urbanism. As the rapidly increase of vehicles in urban, our traditional transportation way was changed radically ,which caused a difficult traffic problem to all of the big city. Intersection is the city-traffic node ,the main location of flowing and the key section for congestion of the city. Statistic shows that one third of runtimes was wasted there, more than 60% accident happen at there and around place. Consequently , to make valid leading on city intersection traffic volume not only can improve the transit efficiency ,relax traffic pressure, but also can advance the continual development of the city.This topic based on the video detection and disposal. The camera used to gather intersection traffic information , then computers pick-up the traffic volume parameter from video images to make image process , which provides the evidences for adaptation control of intersection signals. The thesis according the compare among vehicle detection technology to stand out the advantages of video detection in city traffic. Meantime, it analyses algorithm of vehicle detection to make certain the section of video detection. To adopt exact image information is the precondition for image process. It has the researching details for camera calibration technology and calibration algorithm in this content. With analyzing the key-skills of digital image process which includes image transform, image enhancement ,image recover and image edge detection, comparing the image process effects in Visual C++ platform , and continuously optimize algorithm, the vehicle detection precision be improved .The gathering platform of traffic volume ,vehicle speeds and vehicle queue length be set up according describe capture module of traffic parameter, explain the details of procedure and analyze the requirements of system. It offers the whole thought to intersection signals adaptation control at the same time. Through collecting and deal with intersection field images to obtain experiment data , then analyzing the reason of error and looking for the way to improve this detection . Experience indicates this system keeps certain value on research and application.