Real-time principal component cursuit


Graeme Pope, Manuel Baumann, Christoph Studer, and Giuseppe Durisi


Proc. of Asilomar Conf. on Signals, Systems, and Computers (Asilomar), Pacific Grove, CA, pp. 1433-1437, Nov. 2011.

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Robust principal component analysis (RPCA) deals with the decomposition of a matrix into a low-rank matrix and a sparse matrix. Such decompositions find, for example, applications 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 computational complexity. Furthermore, we perform a systematic analysis of the performance/complexity tradeoffs underlying PCP. For synthetic data, we show that our methods result in a speedup of more than 365 times compared to a base C implementation at only a small loss in terms of recovery performance. In order 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 10 frames per second (fps) using an off-the-shelf PC.

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