  Principal component pursuit (PCP) is a state-of-the-art approach for
background estimation problems. Due to their higher computational cost, PCP
algorithms, such as robust principal component analysis (RPCA) and its
variants, are not feasible in processing high definition videos. To avoid the
curse of dimensionality in those algorithms, several methods have been proposed
to solve the background estimation problem in an incremental manner. We propose
a batch-incremental background estimation model using a special weighted
low-rank approximation of matrices. Through experiments with real and synthetic
video sequences, we demonstrate that our method is superior to the
state-of-the-art background estimation algorithms such as GRASTA, ReProCS,
incPCP, and GFL.
