While Principal Component Pursuit (PCP) is currently considered to be the state of the art method for video background modeling, it suffers from a number of limitations, including a high computational cost, a batch operating mode, and sensitivity to camera jitter. In this paper we propose a novel fully incremental PCP algorithm for video background modeling that is robust to translational and rotational jitter. It processes one frame at a time, obtaining similar results to standard batch PCP algorithms, while being able to deal with translational and rotational jitter. It also has extremely low memory footprint, and a computational complexity that allows almost real-time processing.