  We propose a surprisingly simple model for supervised video background
estimation. Our model is based on $\ell_1$ regression. As existing methods for
$\ell_1$ regression do not scale to high-resolution videos, we propose several
simple and scalable methods for solving the problem, including iteratively
reweighted least squares, a homotopy method, and stochastic gradient descent.
We show through extensive experiments that our model and methods match or
outperform the state-of-the-art online and batch methods in virtually all
quantitative and qualitative measures.
