14 May 2015 Compressed-sensed-domain L1-PCA video surveillance
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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.
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Ying Liu, Ying Liu, Dimitris A. Pados, Dimitris A. Pados, } "Compressed-sensed-domain L1-PCA video surveillance", Proc. SPIE 9484, Compressive Sensing IV, 94840B (14 May 2015); doi: 10.1117/12.2179722; https://doi.org/10.1117/12.2179722
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