Video background modeling is an important preprocessing step in many video analysis
systems. Principal component pursuit (PCP), which is currently considered to be the state-of-
the-art method for this problem, has a high computational cost, and processes a large
number of video frames at a time, resulting in high memory usage and constraining the
applicability of this method to streaming video. In this paper, we propose a novel fully
incremental PCP algorithm for video background modeling. It processes one frame at a time …

Abstract An incremental Principal Component Pursuit (PCP) algorithm for video background
modeling that is able to process one frame at a time while adapting to changes in
background, with a computational complexity that allows for real-time processing, having a
low memory footprint and is robust to translational and rotational jitter.