基于优化k均值建模的运动目标检测算法

在对运动目标检测构建出精准的背景模型的方法中，k均值聚类算法是一种快速且简单有效的划分法，对于大型数据集，可伸缩且高效k均值聚类算法被广泛应用。但是，该算法会对初始聚类中心的变化表现得敏感，聚类中心的变化常会使得算法误差较大。本文将介绍一种对初始聚类中心选择改进法：利用遗传算法能高效地全局搜索出最优解这一特点，克服了k均值聚类算法易陷入局部最优解的缺点。改进后的遗传算法MAGA能快速地提取出最优初始聚类中心，通过实验仿真总结出基于MAGA的k均值聚类建模精确度比较高，对检测小而多的运动目标存在很大优势。

Movingtarget detection algorithm based on k means optimized modeling

On moving target detection construct accurate background modeling method, k means clustering algorithm is a fast and simple and effective classification method, for large data sets, scalable and efficient k means clustering algorithm is widely used. However, the algorithm will be sensitive to the change of the initial clustering center performance, the clustering center changes often makes the algorithm error is bigger. This article introduces an improved method to choose the initial clustering center: by using the genetic algorithm can efficiently global search out optimal solutions to this characteristic, overcome k means clustering algorithm is easily plunged into local optimal solution of the shortcomings. The improved genetic algorithm (MAGA) quickly extract the optimal initial clustering center, through the experimental simulation is summarized on the basis of MAGA k means clustering modeling accuracy is higher, to detect small number of moving targets there is a big advantage.