Incremental Principal Component Pursuit for Video Background Modeling

Article

Abstract

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.

Keywords

Principal component pursuit Video background modeling  Incremental singular value decomposition 

Notes

Compliance with ethical standards

Funding

This research was supported by the “Fondo para la Innovación, la Ciencia y la Tecnología” (Fincyt) Program for author Paul Rodriguez. This research was supported by the U.S. Department of Energy through the LANL/LDRD Program and by UC Lab Fees Research grant 12-LR-236660 for author Brendt Wohlberg.

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Electrical Engineering DepartmentPontificia Universidad Católica del PerúLimaPeru
  2. 2.Theoretical DivisionLos Alamos National LaboratoryLos AlamosUSA

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