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Machine Vision and Applications
Published in: Machine Vision and Applications 5/2014

01-07-2014 | Special Issue Paper

pROST: a smoothed \(\ell _p\)-norm robust online subspace tracking method for background subtraction in video

Authors: Florian Seidel, Clemens Hage, Martin Kleinsteuber

Published in: Machine Vision and Applications | Issue 5/2014

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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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Footnotes
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Metadata
Title
pROST: a smoothed -norm robust online subspace tracking method for background subtraction in video
Authors
Florian Seidel
Clemens Hage
Martin Kleinsteuber
Publication date
01-07-2014
Publisher
Springer Berlin Heidelberg
Published in
Machine Vision and Applications / Issue 5/2014
Print ISSN: 0932-8092
Electronic ISSN: 1432-1769
DOI
https://doi.org/10.1007/s00138-013-0555-4

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