Background Modeling using Mixture of Gaussians for Foreground Detection - A Survey

By Thierry Bouwmans, Fida El Baf, Bertrand Vachon. 2008

564 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/932459675ac2ee07edc309bb7149bc893645428b

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In Artificial Intelligence

In Computer Vision and Pattern Recognition

Citations in the corpus

Evaluation of background subtraction techniques for video surveillance (2011, methodology): “Over the past years, various BS methods have been developed (cf. surveys [24, 9, 4, 2, 3]), with each of them having its own characteristic, strength and weakness.”
Recent Advanced Statistical Background Modeling for Foreground Detection - A Systematic Survey (2011, background): “This paper is an extended and updated paper of the surveys on Mixture of Gaussians for background modeling [48] and Subspace Learning for background modeling [334].”
Background Subtraction in Real Applications: Challenges, Current Models and Future Directions (2019, background): “To have an overview about the fundamental research in this field, the reader can refer to numerous surveys on background initialization [235][236][47][171] and background subtraction methods [46][43][128][45][41][42][50][44][49].”
Dissimilarity-based people re-identification and search for intelligent video surveillance (2013, background): “The interested reader is referred to [38] and [21] for a comprehensive survey of pedestrian detection and foreground segmentation algorithms.”
Decomposition into Low-rank plus Additive Matrices for Background/Foreground Separation: A Review for a Comparative Evaluation with a Large-Scale Dataset (2015, methodology): “Moreover, we believe that we are living in a key transition in the field of background subtraction as we are progressively migrating from the conventional statistical models as MOG [357][33], KDE [95][30] and naive subspace learning models [29] to models based on robust decomposition into low-rank p”
A comprehensive review of background subtraction algorithms evaluated with synthetic and real videos (2014, methodology): “DPGrimsonGMMBGS Gaussian Mixture Model [38] MixtureOfGaussianV1BGS Gaussian Mixture Model [25] MixtureOfGaussianV2BGS Gaussian Mixture Model [49] DPZivkovicAGMMBGS Gaussian Mixture Model [49] LBMixtureOfGaussians Gaussian Mixture Model [6]”
Maximum Margin Criterion (2018, background): “Many background subtraction methods have been developed in video-surveillance to detect moving objects [2][3][4].”
Traditional and recent approaches in background modeling for foreground detection: An overview (2014, result): “So, the previous surveys made by Bouwmans et al. focus only on some categories [38][34] or sub-categories [37][33][40] of background models as follows: is the most common approach .”
Changedetection.net: A new change detection benchmark dataset (2012, methodology): “• Bouwmans et al., 2008 [5] survey only GMMmethods and use the Wallflower dataset.”
CDnet 2014: An Expanded Change Detection Benchmark Dataset (2014, background): “Some cameras apply automatic exposure adjustment resulting in global brightness fluctuations in time.”