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Paul Rodríguez and Brendt Wohlberg, "Incremental Principal Component Pursuit for Video Background Modeling", Journal of Mathematical Imaging and Vision, vol. 55, no. 1, doi:10.1007/s10851-015-0610-z, pp. 1--18, May 2016


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, obtaining similar results to standard batch PCP algorithms, while being able to adapt to changes in the background. It has an extremely low memory footprint, and a computational complexity that allows real-time processing.

BibTeX Entry

author = {Paul Rodr\'{i}guez and Brendt Wohlberg},
title = {Incremental Principal Component Pursuit for Video Background Modeling},
year = {2016},
month = May,
urlpdf = {},
journal = {Journal of Mathematical Imaging and Vision},
volume = {55},
number = {1},
doi = {10.1007/s10851-015-0610-z},
pages = {1--18}