Abstract
An increasing number of methods for background subtraction use Robust PCA to identify sparse foreground objects. While many algorithms use the \(\ell _1\)-norm as a convex relaxation of the ideal sparsifying function, we approach the problem with a smoothed \(\ell _p\)-quasi-norm and present pROST, a method for robust online subspace tracking. The algorithm is based on alternating minimization on manifolds. Implemented on a graphics processing unit, it achieves realtime performance at a resolution of \(160 \times 120\). Experimental results on a state-of-the-art benchmark for background subtraction on real-world video data indicate that the method succeeds at a broad variety of background subtraction scenarios, and it outperforms competing approaches when video quality is deteriorated by camera jitter.
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Seidel, F., Hage, C. & Kleinsteuber, M. pROST: a smoothed \(\ell _p\)-norm robust online subspace tracking method for background subtraction in video. Machine Vision and Applications 25, 1227–1240 (2014). https://doi.org/10.1007/s00138-013-0555-4
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DOI: https://doi.org/10.1007/s00138-013-0555-4