Research And Parallelization Of Vehicle Detection And Tracking Algorithm

Along with our country’s urbanization developed very quickly,the highway traffic system pressure is increasing.Therefore,the importance of intelligent transportation system based on computer vision technology is becoming more and more significant,its core technology is in a stationary camera for moving vehicle detection and tracking in image sequence.Vehicle detection and tracking are involved in a large number of complex graphics,traditional CPU serial processing properties can meet the real time needed for practical application.However,the GPU has powerful parallel computing ability can be used to solve the bottleneck problem as well.In order to satisfy the real-time demand of vehicle detection and tracking in practical application,the thesisusing the GPU platform CUDA compile environment,in view of the vehicle detection and tracking algorithm is used in the course of research and design the parallelization.The main research content is summarized as follows:1.Moving target detection is the basis of the tracking and analysis,so first is to determine the moving targets in video sequences.Background modeling is will be integrated into a large amount of background pixels background model,it is the most commonly used method is to determine the movement target.Mixed Gaussian background modeling is a recognized method to measure the effectiveness and adaptability are relatively well.However,due to its large amount of calculation to real-time implementation,in view of the GPU platform,mining parallelism of Gaussian background modeling algorithm and optimization,to improve the real-time performance is of great significance to extend its application range.thesisby using the GPU platform CUDA compilation environment,from the angle of the parallel adaptive Gaussian background modeling algorithm for parallel improvement.Experimental proved that thesisdesigned parallel algorithm is 10.3 times higher than the efficiency of serial algorithm.2.The binary image connected area is the foundation of the video tracking,prospects in the binary image is connected pixels by certain rules to give the same tag and not connect pixel gives different markup.It in the CPU to process the core algorithms is relatively high efficiency and robustness of the algorithm.But its efficiency and performance of ascension also is relatively limited.Papers to parallel processing as the starting point,designs and realizes a kind of parallel connected component labeling algorithm of binary image in order to improve the connected component tag binary image processing efficiency of the problem.Experimental proved that thesisdesigned parallel algorithm of efficiency than the parallel algorithm of design efficiency increased 8.3 times.Larger improved the tag of the efficiency and robustness of connected component parallel algorithm.3.For multiple vehicle target tracking under the static camera,in the videosequence of consecutive searching and tracking target most similar characteristics of the target area is the core idea.The thesis designs and realizes a static camera parallel processing algorithm for multiple vehicle target tracking,extraction of features is the center of mass of each track the target area and around the center of mass of gray,etc.Tracking according to the experiment results and the acceleration of 10.2 than serial parallel algorithm proved the validity of the algorithm.