Research And Application Of Moving Object Detection And Tracking Algorithm

It is a hot issue that moving object detection and tracking in the field of computer vision. Its main task is to find and locate the target in the video image, and to identify, analyze and judge. Moving and tracking object detection is the research for the purpose of the machine with human visual perception. The moving objects in image sequences can be identified, and to extract important information from the target data in analysis and understand. However, in the process of image acquisition, the image is usually subjected to various kinds of interference, such as the complex image background and occlusion problem caused by the object itself. This paper mainly aims at the new problems in the practical application. From two aspects of target detection and target tracking, this paper expounds the advantages and disadvantages of the existing algorithms, puts forward the improved scheme, and discusses the application of the specific application in the traffic monitoring system.Firstly, the paper studies the moving object detection algorithm based on background modeling and the simple non background modeling method. The frame difference method and optical flow method are introduced in detail in the non background modeling method, and the application scenarios are analyzed. The validity and limitation of the algorithm are demonstrated by experiments. Several typical background modeling algorithms are introduced in detail. The experimental results and analysis are given, and the problems of the algorithm are presented. In view of the shortcomings of the hybrid Gauss modeling algorithm and ViBe background modeling algorithm, this paper puts forward the improved method, and expounds the related theory and the experimental demonstration.In addition, the paper studies the moving object tracking algorithm. This paper selectively introduces some typical tracking algorithms:Camshift tracking algorithm, Calman filter tracking algorithm, tracking algorithm based on compressed sensing theory. The basic principles of each algorithm are given, and the experimental demonstration of the algorithm is demonstrated, and the problems are analyzed. In view of the deficiency of Camshift algorithm, the Kalman filter is introduced into the Camshift tracking algorithm, and some improvements are made.The paper also introduces the video based vehicle detection classification and tracking system, which further verifies the superiority of the improved algorithm. The development of this system can be divided into five stages:hardware design, image preprocessing, moving vehicle detection, moving object classification, moving target tracking. A Adaboost cascade classifier is proposed to calculate the Haar features of the target classification stage to realize the vehicle identification. Finally, according to the needs of the practical application, the test results, data and analysis of the system are given.