结合稳健估计和Meanshift的视频目标跟踪算法

针对Meanshift目标跟踪算法对强噪声环境敏感的问题,提出了一种结合稳健估计和传统Meanshift的修正Meanshift算法.通过稳健估计修正传统Meanshift算法的核概率密度函数,提升Meanshift算法的鲁棒性.针对信噪比分别为60、30和0 db的仿真数据,将传统Meanshift算法和修正Meanshift算法的跟踪轨迹准确性和精度进行对比.结果表明,修正Meanshift算法能够实现目标准确跟踪,且跟踪位置的相对误差在1%以下.对于实际运动目标视频数据,所提算法也可以实现实时跟踪定位,克服了传统Meanshift算法目标跟踪丢失的问题.

Video target tracking algorithm with combining robust estimation and Meanshift

Aiming at the problem that the Meanshift algorithm is sensitive to the strong noise environment, a modified Meanshift algorithm with combining the robust estimation and traditional Meanshift was proposed. The kernel probability density function of traditional Meanshift algorithm was modified through the robust estimation, and the robustness of Meanshift algorithm got improved. Aiming at the simulation data with signal to noise rate(SNR)of 60, 30 and 0 db, the accuracy and precision of the tracking trajectory of both traditional and modified Meanshift algorithms were compared. The results indicate that the modified Meanshift algorithm can achieve the accurate tracking of targets, and the relative error of tracking position is below 1%. For the real video data of moving targets, the proposed algorithm can also realize the real time tracking and positioning, and can overcome the target tracking missing problem of traditional Meanshift algorithm.