基于自适应混合高斯模型的时空背景建模

提出了一种基于自适应混合髙斯模型的时空背景建模方法, 有效地融合了像素在时空域上的分布信息, 改善了传统的混合髙斯背景建模方法对非平稳场景较为敏感的缺点. 首先利用混合髙斯模型学习每个像素在时间域上的分布, 构造了基于像素的时间域背景模型, 在此基础上, 通过非参数密度估计方法统计每个像素邻域内表示背景的髙斯成分在空间上的分布, 构造了基于像素的空间域背景模型; 在决策层融合了基于时空背景模型的背景减除结果. 为了提高本文时空背景建模的效率, 提出了一种新的混合高斯模型髙斯成分个数的自适应选择策略, 并利用积分图实现了空间域背景模型的快速计算. 通过在不同的场景下与多个背景建模方法比较, 实验结果验证了本文算法的有效性.

Spatiotemporal Background Modeling Based on Adaptive Mixture of Gaussians

The background model of traditional mixture of Gaussians is less robust to non-stationary scenes. This paper presents an adaptive spatiotemporal background model, combining the temporal information of per-pixel and the spatial information in the local region. Based on the temporal distribution model learned by mixture of Gaussians, the spatial background model of per-pixel is utilized to construct the spatial distribution of background in the local region by non-parametric density estimation. The robust detection is achieved by fusing the subtraction results separately based on the temporal and spatial background models. Additionally, to improve the computation efficiency, an adaptive selection strategy of the number of components of mixture of Gaussians model is proposed and integral image method is applied to calculate the spatial background model. Experimental comparisons demonstrate the effectiveness of the proposed method.