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Proceedings Paper

Compressed-sensed-domain L1-PCA video surveillance
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Paper Abstract

We consider the problem of foreground and background extraction from compressed-sensed (CS) surveillance video. We propose, for the first time in the literature, a principal component analysis (PCA) approach that computes the low-rank subspace of the background scene directly in the CS domain. Rather than computing the conventional L2-norm-based principal components, which are simply the dominant left singular vectors of the CS measurement matrix, we compute the principal components under an L1-norm maximization criterion. The background scene is then obtained by projecting the CS measurement vector onto the L1 principal components followed by total-variation (TV) minimization image recovery. The proposed L1-norm procedure directly carries out low-rank background representation without reconstructing the video sequence and, at the same time, exhibits significant robustness against outliers in CS measurements compared to L2-norm PCA.

Paper Details

Date Published: 14 May 2015
PDF: 10 pages
Proc. SPIE 9484, Compressive Sensing IV, 94840B (14 May 2015); doi: 10.1117/12.2179722
Show Author Affiliations
Ying Liu, Univ. at Buffalo (United States)
Dimitris A. Pados, Univ. at Buffalo (United States)


Published in SPIE Proceedings Vol. 9484:
Compressive Sensing IV
Fauzia Ahmad, Editor(s)

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