一种基于Mean Shift的快速跟踪算法

针对Mean Shift算法难以跟踪快速运动目标、算法迭代次数多以及耗费时间长的问题,提出了一种基于Mean Shift的快速运动目标检测方法,该方法结合帧差法并融合背景信息来快速检测运动目标；同时提出一种新的相似性度量方法进行初步检测,排除干扰并快速选出符合标准的目标以进行Mean Shift匹配,找出最佳目标。该方法不仅减少了传统方法的迭代次数,缩短了算法所需时间,而且在跟踪实验中取得了较好的跟踪效果,提升了算法的鲁棒性。

Fast Tracking Algorithm Based on Mean Shift Algorithm

A fast moving target detection method based on Mean Shift was proposed for the problems that the Mean Shift algorithm is difficult to track fast moving objects,the number of iterations of the algorithm is too large and the process is time consuming.The method is combined with frame difference method and fuses background information for rapid detection of moving target.A new similarity measure method for preliminary testing was put forward to exclude the interference and fast select targets in accordance with the standard Mean Shift matching,finding out the best target.This method not only reduces the number of iterations of the traditional method,but also reduces the time required for the algorithm,and it achieves better tracking performance in the tracking experiment,which improves the robustness of the algorithm.