基于Mean Shift的背景梯度修正直方图跟踪算法

Background Gradient Corrected Histogram Tracking Algorithm Based on Mean Shift

当图像中的背景发生波动时,修正的背景加权直方图(CBWH)算法在目标跟踪过程中存在波动跟随现象,从而产生波动误差。为了改善背景波动下算法的跟踪效果,减小波动误差,在CBWH算法的基础上提出了一种背景梯度修正直方图(BGCH)算法。利用相邻帧的背景梯度信息对目标模型进行二阶加权修正,提前阻断了CBWH算法的波动跟随过程。实验结果表明,本算法可以减小CBWH算法的波动误差,且对目标形变、模糊的情况,也表现出了更好的鲁棒性。

When background fluctuates in the image,the target tracking process will follow the fluctuation of background with corrected background-weighted histogram(CBWH)algorithm,which will cause fluctuation-errors.In order to improve the tracking effect under fluctuant background and reduce the fluctuation-errors,this paper proposes a background gradient corrected histogram(BGCH)algorithm based on CBWH algorithm.It uses the background gradient information of adjacent frames to perform a second-order correction on the target model,and blocks the fluctuation-following process of the CBWH algorithm in advance.Experimental results show that this method significantly reduces the fluctuation-errors of CBWH algorithm,and also shows better robustness under the condition of target deforming and blurring.