  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}
