Research And Release The Traffic Parameter Colleting System Based On Video-stream

With the acceleration of urbanization, automobile became more and more popular and people are enjoying the convenience that offer, however we are trapped in the bewilderment of traffic congestion. However to directly construct more highway bridges could not to be able to catch up with the development speed of the vehicles. Under the existing condition, enhancing the level of transportation control and the management, the reasonable use existing traffic facility, and fully displaying its performance is the effective methods to solve transportation question. Along with the computer hardware technology and the computer vision technology development, a computer vision-based traffic monitoring system has become possible. Vehicle detection and tracking real-time system based on video is the key to traffic monitoring system. Many popular related technology didn't meet vary requirements. Therefore, an efficient and robust detection and tracking system is needed eagerly. For these all, our research is mainly in following:This thesis used the auto-adapted background model based on mixture of the Gaussians to distinguish foreground object and the background, thus carried on the detection to the foreground movement object. At the same time we propose that with morphology filter we avoid the shortcoming of ignore the relationship between the pixels.In track aspect this thesis used track model based on expanded kalman filtering, carried on the track to the object. By expands the kalman filtering model, reduced the hunting zone to increase the search precision and the algorithm efficiency. And we use color histogram to match the image of object area between the continuous frames.In the transportation parameter survey aspect, we firstly transform the screen coordinates into the real world coordinates, then with the real world coordinates we could survey the speed and simply classify the vehicle.Finally, the algorithm of vehicle detection and tracking used in this paper can limit the noise of system and pedestrian factor, and used in large area, multiple objects and complex environment in traffic surveillance. And the implementation can be applied in the highway and in the municipal transportation management.