作　　者： ; ; ; ;

机构地区： 哈尔滨工业大学航天学院空间光学工程研究中心

出　　处： 《光学精密工程》 2010年第3期764-770,共7页

摘　　要： 提出了一种改进的均值移位红外目标跟踪算法,该算法融合了基于均值移位的梯度匹配搜索策略与基于特征分类的跟踪算法。以目标与局部背景灰度特征的似然比作为目标区域核直方图的权值,建立了改进的目标表征模型。以Bhattacharyya系数作为相似性度量,在均值移位框架下推导了应用该目标模型下移位向量的表达形式。同时,提出了基于跟踪复杂度估计的目标遮挡情况下的模型更新判别准则。实验结果表明,该算法能够提高目标像素灰度的移位权重,抑制背景干扰,对于低对比度红外目标的跟踪具有稳健的性能,在正确跟踪情况下平均Bhattacharyya系数保持在0.97以上。 An improved IR target tracking algorithm based on Mean Shift is proposed combined the mean-shift based gradient matched searching strategy with the feature-classification based tracking algorithm. An improved target representing model is set up by taking the likelihood ratio of gray level features of a target and a local background as a weighted value of the original kernel histogram of target area. The expression of mean-shift vector in this target model is deduced,when Bhattacharyya coefficients are regarded as the similarity measures. Meanwhile, the criterion of model updating based on tracking complexity estimation under target occlusion is presented. The experimental result indicates that the algorithm can improve the shift weight of target pixel gray level and can suppress the background interference, therefore the tracking performance of the low contrast IR target is robust and the average Bhattacharyya coefficients can keep above 0.97 in a correct tracking case.

关 键 词： 信息处理 红外目标跟踪 均值移位 似然比

领　　域： [自动化与计算机技术] [自动化与计算机技术]