摘要
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,提出了相应的改进措施。其一,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,从而有效地减小背景像素的影响。另外,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,由此很好地实现了目标部分遮挡情况下的稳定跟踪。当目标被严重遮挡时,则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:提出的改进算法可以准确地进行目标跟踪,对部分遮挡和严重遮挡都有较强的鲁棒性。
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 same way.By above operation,the effect of background pixels on the image has reduced greatly.Secondly,the whole candidate region is separated into several overlapped fragments,and every fragment is iterated by the Mean-shift.Then,the object localization is reset by the location of fragment in the candidate region,which matches mostly to the corresponding fragment in the object region.Experimental results show that the fragment based on the Mean-shift is very robust to partial occlusion.Furthermore,when object is severely occluded,the linear prediction can be used to estimate the probable location of the object in the next frame.These results prove that the tracking using the improved Mean-shift algorithms has good localization precision and is robust to partial and severe occlusions.
出处
《光学精密工程》
EI
CAS
CSCD
北大核心
2010年第1期234-239,共6页
Optics and Precision Engineering
基金
国家863高技术研究发展计划资助项目(No.2005AA778032)
