提出一种利用背景聚类的快速前景分割算法。该算法首先通过一种专门用于背景聚类的无监督模糊聚类方法将历史像素值进行聚类，继而用高斯成分来模拟每一个聚类，构建了基于聚类的时间域的背景模型。前景的分割则采用阈值化方法对像素属于背景的概率进行二分化处理。由于该方法能够根据场景自适应确定背景为单模或多模分布，避免了耗时的背景模型构建和更新过程，因而减少了内存使用量并提高了检测速度。对于多种场景下的不同视频进行实验，结果表明该算法能够在保持检测精度的同时，大幅提高检测速度。

A statistical background subtraction technique is proposed based on clustering of temporal color/intensity. An un-supervised clustering method is proposed to model a background with serial of clusters. The unimodal or multimodal distributions of background are detected adaptively. We use a Gaussians model to simulate each cluster which prevents the estimation the parameter of mix of Gaussians model. The foreground will be detected by comparing the background possibility with a threshold. Experimental results show our approach has equal or better segmentation performance and is proved capable of real-time processing.