Incremental principal component pursuit for video background modeling
Abstract
An incremental Principal Component Pursuit (PCP) algorithm for video background modeling that is able to process one frame at a time while adapting to changes in background, with a computational complexity that allows for real-time processing, having a low memory footprint and is robust to translational and rotational jitter.
- Inventors:
- Issue Date:
- Research Org.:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1346950
- Patent Number(s):
- 9595112
- Application Number:
- 14/722,651
- Assignee:
- STC.UNM
- Patent Classifications (CPCs):
-
G - PHYSICS G06 - COMPUTING G06T - IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- DOE Contract Number:
- AC52-06NA25396
- Resource Type:
- Patent
- Resource Relation:
- Patent File Date: 2015 May 27
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 97 MATHEMATICS AND COMPUTING
Citation Formats
Rodriquez-Valderrama, Paul A., and Wohlberg, Brendt. Incremental principal component pursuit for video background modeling. United States: N. p., 2017.
Web.
Rodriquez-Valderrama, Paul A., & Wohlberg, Brendt. Incremental principal component pursuit for video background modeling. United States.
Rodriquez-Valderrama, Paul A., and Wohlberg, Brendt. Tue .
"Incremental principal component pursuit for video background modeling". United States. https://www.osti.gov/servlets/purl/1346950.
@article{osti_1346950,
title = {Incremental principal component pursuit for video background modeling},
author = {Rodriquez-Valderrama, Paul A. and Wohlberg, Brendt},
abstractNote = {An incremental Principal Component Pursuit (PCP) algorithm for video background modeling that is able to process one frame at a time while adapting to changes in background, with a computational complexity that allows for real-time processing, having a low memory footprint and is robust to translational and rotational jitter.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2017},
month = {3}
}
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