Aimed at the disadvantage that background subtraction was liable to be affected by outside environment, a background modeling method based on the improved K-mean clustering was provided. By comparing the distances between certain sample and sub-class center of the pixel, observation values of the pixels were clustered and the number of the clusters was determined during the clustering process.. After learning for a period, background model was built by sub-class with the maximum samples. Simulations show that actual background can be extracted accurately by the algorithm even when moving targets are existent and the memory cost of the system is reduced dramatically

基于改进K_均值聚类算法的背景建模方法_杨会锋.pdf

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