高效目标跟踪研究：基于改进Mean Shift与粒子滤波的快速方法

he study in this paper focuses on the improvement of the Mean Shift algorithm for motion object detection and tracking. By combining the Mean Shift algorithm with particle filtering, the researchers aim to overcome the problem of particle scarcity in tracking moving objects. The Mean Shift algorithm is used to quickly locate the approximate region of the target, and then particle filtering is employed to precisely track the object. This approach reduces the wastage of particles and increases the number of particles with high weights, effectively addressing the issue of particle scarcity in particle filtering. This method is particularly suitable for tracking small and fast-moving targets. The key technologies involved in this research include the Mean Shift algorithm, Lucas-Kanade optical flow method, object detection, object tracking, and particle filtering. By combining these technologies, the researchers propose a fast motion object tracking method that can efficiently track small and fast-moving targets. In conclusion, the research presented in this paper offers a novel approach to improving the Mean Shift algorithm for motion object detection and tracking. By addressing the issue of particle scarcity in particle filtering, the proposed method demonstrates promising results in tracking small and fast-moving targets. This research contributes to the advancement of technologies in the field of machine vision and paves the way for more efficient and accurate motion object detection and tracking systems.