一种改进相似度量的红外目标跟踪算法

针对红外目标成像特点,提出了一种简单易行、鲁棒性强的目标跟踪算法.在对原始算法中的核心部分相似度量函数进行理论分析的基础上,新算法给出了两点改进:一是利用动态聚类思想对相似度量函数中匹配样本增加动态权值,提高了模型图像和目标图像样本间匹配准确度,二是增加了模型图像和目标图像像素点邻域信息,进一步增强跟踪算法鲁棒性.实验仿真表明,新建算法能对复杂场景下的红外目标进行较好跟踪,证实了跟踪算法的可行性和有效性.

An imprAn Improved Similarity Measure Based IR Target Tracking Algorithm

Based on the characteristics of IR target imaging,a simple to implement,good robustness target tracking algorithm is proposed,which is the result of a theoretical analysis of the similarity measure function of the core component of the original algorithm.Two improvements are given.First,based on the idea of clustering,dynamic weighted coefficients are added to the matching samples in similarity measure function,which,in effect,improves the matching accuracy of model image and target image.Second,the pixel neighborhood information of model image and target image is augmented to the original model,which,in turn,further improves the tracking algorithm′s robustness.Experimental results show that the new algorithm can achieve good result of tracking IR targets under complex scenery,and this demonstrates the feasibility and effectiveness of the algorithm.