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.
Video background tracking and foreground extraction via L1-subspace updates / Pierantozzi, Michele; Liu, Ying; Pados, Dimitris A.; Colonnese, Stefania. - STAMPA. - 9857:(2016). (Intervento presentato al convegno Compressive Sensing V: From Diverse Modalities to Big Data Analytics tenutosi a Baltimore, Maryland, United States) [10.1117/12.2224956].
Video background tracking and foreground extraction via L1-subspace updates
COLONNESE, Stefania
2016
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.File | Dimensione | Formato | |
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Pierantozzi_Video-background_2016.pdf
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