The Study Of Traffic Flow Detection Technologies Based On Digital Image Technology

With the rapid development of the modern economy, keeping traffic and transportation safe and orderly is becoming more and more important, and the demand for traffic management is higher than before. To protect traffic condition, improve transportation environment, and accelerate economic development, Intelligent Traffic System which is based on computer science and communication technology managing traffic and detecting vehicles is created. Technology of the vehicle detection is the key to the series of technologies for the intelligent traffic system. Research on the technology for the vehicle detection based on image processing is just the hot of the field.Research on detecting vehicles based on image processing began on 1980s. Up to now, detecting ideas and detecting technology has been updating. Some researches are based on detecting region, and some are based on the whole picture such as background difference method and frame difference method.A new traffic flow detection algorithm based on edge information with limit progress is proposed in this paper after systematic analysis and comparison of several fashionable detection-related image preprocessing and recognition algorithm and flow detection methods, which background difference method, frame difference method, road mark method and edge detection technology are listed in the consideration. Edges of an image reflect the information of the image mostly including the basic characteristics. Edge detection is a vital part of many image processing and pattern recognition systems. Typical areas of applications are image segmentation, stereo vision, and identification of objects as in automatic target recognition. Edge detection is one of the most important parts in image processing. We believe that this algorithm owns merits of robust adaptation, small calculation; precise and strong stability under certain circumstance for it inherits from both advanced frame difference method and improved edge detection technology.In the end, the limitation has been pointed out as this design and the way to improve. Certainly, we should continue to make research on this topic.