Compressed-sensed-domain L1-PCA video surveillance

Ying Liu; Dimitris A. Pados

doi:10.1117/12.2179722

14 May 2015 Compressed-sensed-domain _L1-PCA video surveillance

Ying Liu, Dimitris A. Pados

Proceedings Volume 9484, Compressive Sensing IV; 94840B (2015) https://doi.org/10.1117/12.2179722
Event: SPIE Sensing Technology + Applications, 2015, Baltimore, MD, United States

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.

Citation Download Citation

Ying Liu and Dimitris A. Pados "Compressed-sensed-domain _L1-PCA video surveillance", Proc. SPIE 9484, Compressive Sensing IV, 94840B (14 May 2015); https://doi.org/10.1117/12.2179722

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