在线特征融合的均值移位红外目标跟踪

提出了一种改进的均值移位红外目标跟踪算法.首先,针对红外图像低信噪比的特点,采用局部灰度均值特征及局部标准差特征用于目标建模.其次,针对目标低对比度的特点,以目标与局部背景的特征似然比作为核直方图的权值,建立了新的特征表征模型,并将两种特征模型进行线性融合,得到最终的目标表征模型,其中的融合系数由特征似然图对比度自适应确定.最后,在均值移位框架下推导了该模型梯度匹配过程中移位向量的表达形式.同时,基于帧间综合对比度的变化建立了复杂背景条件下的模型更新判别准则.通过基于实测数据的红外目标跟踪实验验证了该算法的可行性.

Mean-shift tracking for IR characteristics of target based on online feature fusion

An improved mean shift tracking algorithm for IR target was proposed.Firstly,the local gray mean feature and local standard deviation feature were utilized to reafize target modeling based on the low SNR characteristic of IR images.Secondly,according to the low contrast feature of target,the new feature-representing model was established where the feature likelihood ratios of target and local background were regarded as the weight value of kernel histogram.The final target representation model was obtained by means of linear fusing the two feature models,and the fusion coefficient was determined adaptively by contrast ratio of feature likelihood map.And lastly,the expression of shift vector in the process of the model gradient matching was derived in the framework of mean shift.Meanwhile,the discrimination criterion of model updating based on inter-frame change of the comprehensive contrast under complex background was constructed.The validity and the feasibility of the algorithm are proved by the actual experiments of IR target tracking.