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An Improved Background Reconstruction Algorithm Based on Basic Sequential Clustering

机译：一种基于基本序列聚类的改进的背景重构算法

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摘要

Based on the assumption that background appears with large appearance frequency, a new background reconstruction algorithm based on basic sequential clustering is proposed in this research. First, pixel intensity in period of time are classified based on mend basic sequential clustering. Second, merging procedure and reassignment procedure are run to classified classes. Finally, pixel intensity classes, whose appearance frequencies are higher than a threshold, are selected as the background pixel intensity value. So the improved algorithm can rebuilt the background images of various scenes. Compared with the background reconstruction method based on basic sequential clustering, the simulation results show that those near classes are avoided at all and the effect of input order of data has been reduced greatly in our method. And the background model can represent the scene well.
机译：基于背景出现频率大的假设，提出了一种基于基本序列聚类的背景重建算法。首先，基于修补的基本顺序聚类对时段内的像素强度进行分类。其次，对分类类别执行合并过程和重新分配过程。最后，将出现频率高于阈值的像素强度等级选择为背景像素强度值。因此，改进算法可以重建各种场景的背景图像。与基于基本序列聚类的背景重构方法相比，仿真结果表明，该方法完全避免了近类，并且大大降低了数据输入顺序的影响。并且背景模型可以很好地表示场景。

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