一种鲁棒的基于改进Mean-shift的目标跟踪算法

为了克服传统Mean-shift算法在跟踪运动目标时由于背景像素造成的定位偏差和由于遮挡造成的跟踪失效，提出了两点改进措施。第一，根据初始帧目标和背景在颜色分布上的差异，建立对数似然图(log-likelihood image)，筛选出目标中与背景可区分性好的颜色特征建立目标模型，并以同样的方法在后续帧建立候选模型。第二，将候选区域划分为若干重叠的子块，分别利用Mean-shift算法对各个子块进行迭代，以与目标区域相应子块最为匹配的子块的所在位置对整个目标重新定位，该方法很好地实现了目标部分遮挡情况下的稳定跟踪；严重遮挡时，采用简单的线性预测，估计下一帧目标可能出现的位置。实验结果表明：改进算法可以准确的进行目标跟踪，对部分遮挡和严重遮挡都有较强的鲁棒性。

Robust object tracking based on improved mean-shift algorithm

To overcome the shortcomings of the traditional Mean-shift algorithm for object tracking such as the localization error caused by background pixels and the tracking failure from the object occlusion,an improved Mean-shift algorithm is proposed.Firstly,according to the difference of color distribution between the object and the background in the initial frame,a log-likelihood image is set up to select the discriminative color features for object modeling,and then the candidate modeling is established by the ...