Research On Moving Object Tracking Based On Mean Shift And Particle Filter Algorithm

Moving object tracking based on the video is a hot research topic in the field of computer vision, has been more widely used in public safety, military guidance and traffic monitoring. Moving object tracking algorithm has direct impact on the effect of video tracking accuracy and stability, robustness and precision tracking method has been key issues in the field of computer vision.This paper briefly describes the currently used algorithms of moving object tracking, given the principle of these algorithms and their advantages and disadvantages, and then the paper focuses on the Mean Shift algorithm,which is more popular algorithm in recent years in the field of moving object tracking,describe its principles and applications in the field of target tracking.Mean Shift algorithm based on single color features to describe the target being tracked by a complex environment such as the light changes, similar to the background interference, this paper proposes a kind of Mean Shift tracking algorithm based on multi-feature fusion, local binary pattern (LBP)texture features and color features are combined to describe the moving object to improve the robustness of the Mean Shift tracking algorithm.Traditional Mean Shift algorithm can not effectively tracking the mocing target of high-speed motion, this paper proposes a high-speed motion tracking algorithm based on Mean Shift and particle filter algorithm. First proposed a multi-feature fusion particle filter algorithm to improve the tracking accuracy of the particle filter tracking algorithm, in order to more effectively coarse positioning moving object position, then improved Mean Shift algorithm for precise positioning of the target, so as to achieve the purpose of tracking the high-speed moving target.Finally, this paper design a systems based on Mean Shift algorithm, relying on the Texas Instruments (TI) DaVinci TMS320DM6467chip to build the moving object tracking system.