Real-Time Principal Component Pursuit
Paper i proceeding, 2011

Robust principal component analysis (RPCA) deals with the decomposition of a matrix into a low-rank matrix and a sparse matrix. Such a decomposition finds, for example, applica- tions in video surveillance or face recognition. One effective way to solve RPCA problems is to use a convex optimization method known as principal component pursuit (PCP). The corresponding algorithms have, however, prohibitive computational complexity for certain applications that require real-time processing. In this paper we propose a variety of methods that significantly reduce the computational complexity. Furthermore, we perform a systematic analysis of the performance/complexity tradeoffs underlying PCP. For synthetic data, we show that our methods re- sult in a speedup of more than 365 times compared to a reference C implementation at only a small loss in terms of recovery error. To demonstrate the effectiveness of our approach, we consider foreground/background separation for video surveillance, where our methods enable real-time processing of a 640×480 color video stream at 12 frames per second (fps) using a quad-core CPU.


Graeme Pope

Manuel Baumann

Christoph Studer

Giuseppe Durisi

Chalmers, Signaler och system, Kommunikation, Antenner och Optiska Nätverk

Proc. Asilomar Conf. Signals, Syst., Comput. Pacific Grove CA, U.S.A., Nov. 2011


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Datorseende och robotik (autonoma system)

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