Fuzzy Background Modeling

Critical situations met in video surveillance generate imprecision and uncertainties in the process of background modeling Therefore, some authors have recently introduced fuzzy concepts in the step of background modeling as follows:

• • Fuzzy Running Average (7 papers)

  • Fuzzy Color Histograms (FCH) (12 papers) (Features)

  • Type-2 Fuzzy Mixture of Gaussians (T2-FMOG) (7 papers)

  • Kernel Induced Possibilistic Fuzzy (1 paper)

  • Fuzzy Neural Networks (FNN) (6 papers)

  • Fuzzy Estimation (FE) (2 papers)

  • Fuzzy Clustering (1 paper)

  • Fuzzy Markov Field (FMF) (1 paper) (Post-processing)

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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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