Video background tracking and foreground extraction via L1-subspace updates

Michele Pierantozzi; Ying Liu; Dimitris A. Pados; Stefania Colonnese

doi:10.1117/12.2224956

4 May 2016 Video background tracking and foreground extraction via L1-subspace updates

Michele Pierantozzi, Ying Liu, Dimitris A. Pados, Stefania Colonnese

Proceedings Volume 9857, Compressive Sensing V: From Diverse Modalities to Big Data Analytics; 985708 (2016) https://doi.org/10.1117/12.2224956
Event: SPIE Commercial + Scientific Sensing and Imaging, 2016, Baltimore, MD, United States

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.

Citation Download Citation

Michele Pierantozzi, Ying Liu, Dimitris A. Pados, and 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); https://doi.org/10.1117/12.2224956

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