Research On Some Key Techniques Of Urban Traffic Surveillance And Control System Based On Multi-information Fusion

Urban traffic surveillance and control system is one of the most important part in intelligent transportation system, the traditional mode of traffic surveillance and control has been unsuitable to the need of increasing vehicles in urban. In this project, three kinds of techniques, including vehicle detector based on electromagnetism induction, radio frequency identification and digital image processing, are used in urban traffic surveillance and control system. By fusing the information of these sensors, the urban traffic can be monitored and controlled intelligently. In this dissertation, some key techniques, including image processing, information fusion and intelligent control are studied in traffic surveillance and control system.The main content of the dissertation involves:The approach to background modeling based on color grads of different frames is proposed. Aiming at some features of the videos in traffic scene, the image is divided into some sub-regions, every sub-region's background is established according to the color grads, then the background image is formed by integrating sub-region's background. According to the background image, the moving targets are extracted by using the background subtraction algorithm, and the image is filtered by morphology filter after processed by dynamic threshold segmentation method, then targets'shadows are detected according as the feature of colors. Experimental results show this method can be suitable to the need of traffic conditions.The improved approach to vehicles'tracks detection is presented, it includes classify targets firstly, tracking targets in succession, then traffic violations are determined. According to two features of area and the degree of shape complex, the targets are classified by using fuzzy clustering algorithm. Some restricted conditions are formed according to some features of urban traffic, and aiming at the vehicles after classification, measure function is established by using distance, average values of gray and difference of areas, then the best matching objects are determined by finding global optimization between two adjacent images, and the vehicles are tracked. Experimental results show targets can be classified and tracked effectively by these features.The method about automatic vehicle license plate recognizition by fusing information from panoramic camera and close-range camera is presented. Videos in an intersection are taken synchronously by panoramic camera and close-range camera, and the images sampled by close-range camera are used to recognize vehicle license plate. Edges in the image are detected by using improved color Sobel gradient operator, then the binary image is processed by improved filters in mathematical morphology. A fuzzy neural network is built by using multi features as inputs, and this network can locate license plate region accurately. The plate region is converted to binary image and rectified, and the characters are segmented according to the ratio of width to height. Then characters are recognized by template matching algorithm, and some similar characters are grouped and matched in shift many times, the results are the matching characters with best matching.The approach to adopting radio frequency identification in urban traffic surveillance and control system is proposed, and vehicle information and data managing mode are planned. In this dissertation, some functions in this system are discussed after fusing multi-information, which are difficult to be achieved in traditional traffic managing mode, and some functions are simulated in experiments.Simulate analysis is carried out to optimize traffic signal timing in single intersection by applying with fuzzy logic controller. A parameter including vacant time and the number of delayed vehicles is presented, the traffic signal control strategy can be reckoned according to this parameter, and on the basis of the reckoning results, the universes of input fuzzy set are adjusted to the need of timing in various traffic conditions. The results in simulation experiments show that this method is effective.