Using dominant eigenvectors for background modeling (usually known as Eigenbackground) is a common technique in the literature. However, its results suffer from noticeable artifacts. Thus have been many attempts to reduce the artifacts by making some improvements/enhancement in the Eigenbackground algorithm. In this paper, we show the main problem of the Eigenbackground is in its own core and in fact, it is not a good idea to use strongest eigenvectors for modeling the background. Instead, we propose an alternative solution by exploiting the weakest eigenvectors (which are usually thrown away and treated as garbage data) for background modeling. MATLAB codes are available at \url{https://github.com/mamintoosi/Eigenbackground-Revisited}

翻译：文献中常见的一种常见技术是使用占支配地位的源生物来进行背景建模(通常称为Eigenbackround),但是其结果有明显的文物,因此多次试图通过在Eigenbackround 算法中进行某些改进/增强来减少文物。在本文中,我们展示了Eiggenbackround的主要问题在于其本身的核心,事实上,使用最强的源生物来进行背景建模并不是一个好主意。相反,我们提出了另一种解决办法,即利用最弱的源生物(通常被丢弃并被当作垃圾数据处理)来进行背景建模。 MATLAB 代码可在\url{https://github.com/mamintoosi/Eigenbround-Revisted}查阅。