4 May 2016 Video background tracking and foreground extraction via L1-subspace updates
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Abstract
We consider the problem of online foreground extraction from compressed-sensed (CS) surveillance videos. A technically novel approach is suggested and developed by which the background scene is captured by an L1- norm subspace sequence directly in the CS domain. In contrast to conventional L2-norm subspaces, L1-norm subspaces are seen to offer significant robustness to outliers, disturbances, and rank selection. Subtraction of the L1-subspace tracked background leads then to effective foreground/moving objects extraction. Experimental studies included in this paper illustrate and support the theoretical developments.
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Michele Pierantozzi, Michele Pierantozzi, Ying Liu, Ying Liu, Dimitris A. Pados, Dimitris A. Pados, Stefania Colonnese, Stefania Colonnese, } "Video background tracking and foreground extraction via L1-subspace updates", Proc. SPIE 9857, Compressive Sensing V: From Diverse Modalities to Big Data Analytics, 985708 (4 May 2016); doi: 10.1117/12.2224956; https://doi.org/10.1117/12.2224956
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