Background modeling (or representation) describes the kind of model used to represent the background.
The simplest way to model the background is to acquire a background image which doesn't include any moving object. In some environments, the background isn’t available and can always be changed under critical situations like illumination changes, objects being introduced or removed from the scene. To take into account these problems of robustness and adaptation, prospective background modeling methods can be classified in the following categories:
1. Basic Models (6 papers)
Pixel Intensity Classification (PIC) (5 papers)
Pixel Change Classification (PCC) (1 papers)
2. Statistical Models (15 papers)
Co-occurrence (CO) (9 papers)
Pursuing Dynamic Spatio-Temporals Models (STDM) (3 papers)
Projection Pursuit Density Estimation (PPD) (1 paper)
Sample-based Models (SBM) (2 papers)
3. Others Models (16 papers)
Confidence Measure (3 papers)
Linear Model (2 papers)
Linear Dynamical System (1 paper)
Data-Driven Control (3 papers)
Nested Models (1 paper)
Polynomial Models (4 papers)
PID Models (1 paper)
Imbalanced Learning Models (1 paper)
Author: Thierry BOUWMANS, Associate Professor, Lab. MIA, Univ. La Rochelle, France.
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As this website gives many information that come from my research, please cite my following survey papers:
T. Bouwmans, “Traditional and Recent Approaches in Background Modeling for Foreground Detection: An Overview”, Computer Science Review, 2014. [pdf]
T. Bouwmans, E. Zahzah, “Robust PCA via Principal Component Pursuit: A Review for a Comparative Evaluation in Video Surveillance”, Special Issue on Background Models Challenge, Computer Vision and Image Understanding, CVIU 2014, Volume 122, pages 22–34, May 2014. [pdf]
T. Bouwmans, "Recent Advanced Statistical Background Modeling for Foreground Detection: A Systematic Survey", Recent Patents on Computer Science, Volume 4, No. 3, pages147-176, September 2011. [pdf]
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