Research On Traffic Flow Detection Of HD Video

With the advance of image processing,computer vision, pattern recognition,artificial intelligence and other disciplines, traffic parameter detection technology based on video has became the new direction of computer vision field which contains high academic value and significant theory.In this paper, the video images captured by a fixed camera in intersection were used to be objects for this study. The moving target detection and tracking in image sequence was studied in depth and we completed real-time detection of moving targets and on this basis, using VC+ +6.0 and the Open CV library visual image development kit, then we finished the video traffic flow measurement system and obtain good experimental results.Video motion detection algorithm is the mainstream of traffic flow parameters detection algorithm. By analyzing the features and advantages of some moving target detection algorithms such as the background subtraction method,Gauss-Bayesian modeling, frame difference method,edge detection algorithms, we propose a Comprehensive Traffic Flow Detection Model algorithm. Use Gaussian mixture model and Bayesian strategy to do traffic flow parameters of the detection and tracking for standard definition video; while for the high-definition video (pixel value of HD video is 2592×1936, or larger), in order to solve the problem of large computation when using the method of background modeling in the high-resolution images, a new algorithm is proposed which is based on intensity curve of high-resolution image in traffic detection. First of all, set the detection areas, then the intensity curves of the background image and current frame are drawn, which are used to detect the presence of vehicles, so it can solve the problem that time-consuming is enormous and vehicles can not be detected in real-time in high-resolution images.The experiments show that the algorithm is real-time, simple and effective which can accomplish the sub-drive traffic statistics of standard definition video and high-resolution video in real time. This algorithm can accomplish the vehicle tracking,speed determination,trajectory generation,retrograde,overtaking in the video within 500,000 pixels. According to field test of 48 hours, the accuracy of traffic flow statistics reached 92% and the accuracy of speed reached 80%. The accuracy of traffic lane share, retrograde drive detection, driving direction and speeding reached 90%. In the HD video, traffic flow can be detected in real-time and the accuracy is up to 99.2%.