基于交叉协方差子空间估计的前景检测方法

A Foreground Detection Method Based on Cross-Covariance Subspace Estimation

提出了一种基于交叉协方差子空间估计的背景建模方法,用以实现复杂场景下的前景检测.基于交叉协方差的主成分分析方法可以保留更多的图像协方差信息,因此非常适合用于背景模型的构建.本文首次将基于交叉协方差的二维主成分分析方法引入至背景建模领域,并且提出了相应的增量更新算法来实现背景的自适应估计.此外,本文考虑了前景的稀疏性及连续性,并将其合理应用于前景检测过程中.定量实验和定性分析表明,本文提出的方法具有较强的鲁棒性,可以实现复杂场景下的准确背景建模.

In this paper,a novel background modeling method was proposed based on crosscovariance subspace estimation to detect foreground in complex scenarios.The cross-covariance based 2 DPCA（2 dimensional principal component analysis）method can preserve more image covariance information,which makes it suitable for background modeling.Therefore the crosscovariance based 2 DPCA method was introduced into background modeling field and a correlative incremental algorithm was proposed for adaptively estimating background.Considering the sparsity and the continuity of the foreground,the method was used in foreground detecting accurately.Quantitative experimental and qualitative analysis results show that the proposed method can estimate the background information accurately and robustly in complex scenarios.