基于自适应最优聚类的目标匹配跟踪算法

提出了一种基于新的自适应最优聚类的模板匹配跟踪方法。利用模式分类准则计算最优聚类数, 然后根据最优聚类数采用k-均值方法进行多次聚类。根据聚类结果计算熵矢量和距离矢量, 组合得到特征矢量, 利用特征矢量进行匹配跟踪。匹配采用简单的相似性准则, 实时模板更新算法为多模更新。测试结果表明, 该算法针对不同的目标能自适应地选择聚类参数, 在目标发生几何变化时, 能实现精确稳定的跟踪。

Target Matching Tracking Algorithm Based on Adaptive Optimal Clustering

A new template matching algorithm was proposed to solve the problem of tracking targets with attitudes changing violently. It applied k-means algorithm to make multiple clustering based on the optimal number of clusters which was calculated with pattern classification criteria. The features vector for matching tracking by combining the entropy vector with the distance vector, both of which were calculated according to the clustering result. It adopted a simple similarity criterion to realize matching, while used a multimode updating algorithm to update real-time reference template. Experimental results certificate the new algorithm is able to calculate the number of clusters adaptively. Additionally, the new algorithm is able to track geometrically changing targets precisely and stably.