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Background Modeling Based on Region Segmentation

机译：基于区域分割的背景建模

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Background modeling is an important problem in automated video surveillance systems. Nonparametric models have promising results. But these models have high computational load and large memory requirement because a large set of background samples is usually needed to model the background. In this paper, a background model based on region segmentation is proposed. An Adaptive single Gaussian background model is used in the Stable Region with gradual changes and nonparametric model is used in the Variable Region with jumping changes. A Generalized Agglomerative Scheme is used to merge the pixels in the Variable Region and fill the small interspaces. A Two-Threshold Sequential Algorithmic Scheme is used to group the background samples of the Variable Region into distinct Gaussian distributions. The kernel density computation complexity is largely reduced by arranging the computation order of these groups according to their proximity in mean value to the current pixel sample being estimated. Experimental results show that the proposed method is computationally more efficient than existing nonparametric model, but achieves a comparable result.
机译：背景技术建模是自动视频监控系统中的一个重要问题。非参数模型具有很有希望的结果。但这些模型具有高计算负荷和大的内存要求，因为通常需要大量的背景样本来建模背景。本文提出了一种基于区域分割的背景模型。在具有逐渐变化的稳定区域中使用自适应单个高斯背景模型，并且在具有跳跃变化的可变区域中使用非参数模型。广义附聚方案用于合并可变区域中的像素并填充小型间隔。双阈值连续算法方案用于将变量区域的背景样本分组成不同的高斯分布。通过将这些组的计算顺序根据其在估计的当前像素样本的平均值，通过将这些组的计算顺序排列而大大降低了内核密度计算复杂度。实验结果表明，该方法的计算方式比现有的非参数模型更有效，但实现了相当的结果。

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