Proceedings Paper
Video background tracking and foreground extraction via L1-subspace updates| Format | Member Price | Non-Member Price |
|---|---|---|
| $17.00 | $21.00 |
Paper 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.
Paper Details
Date Published: 4 May 2016
PDF: 16 pages
Proc. SPIE 9857, Compressive Sensing V: From Diverse Modalities to Big Data Analytics, 985708 (4 May 2016); doi: 10.1117/12.2224956
Published in SPIE Proceedings Vol. 9857:
Compressive Sensing V: From Diverse Modalities to Big Data Analytics
Fauzia Ahmad, Editor(s)
PDF: 16 pages
Proc. SPIE 9857, Compressive Sensing V: From Diverse Modalities to Big Data Analytics, 985708 (4 May 2016); doi: 10.1117/12.2224956
Show Author Affiliations
Michele Pierantozzi, La Sapienza Univ. of Rome (Italy)
Ying Liu, State Univ. of New York at Buffalo (United States)
Ying Liu, State Univ. of New York at Buffalo (United States)
Dimitris A. Pados, State Univ. of New York at Buffalo (United States)
Stefania Colonnese, La Sapienza Univ. of Rome (Italy)
Stefania Colonnese, La Sapienza Univ. of Rome (Italy)
Published in SPIE Proceedings Vol. 9857:
Compressive Sensing V: From Diverse Modalities to Big Data Analytics
Fauzia Ahmad, Editor(s)
© SPIE. Terms of Use
