ViBE: A powerful random technique to estimate the background in video sequences

By Olivier Barnich, Marc Van Droogenbroeck. IEEE International Conference on Acoustics, Speech, and Signal Processing, 2009

434 citations recorded by the source corpus.

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

People detection and tracking from aerial thermal views (2014, background): “Additionally, reasoning between different, correctly classified objects becomes more difficult since texture information is rare.”
Evaluation of background subtraction techniques for video surveillance (2011, background): “Barnich 6 [ 1 ] 2009 non-parametric/non-recursive color pixel 7 yes DM”
Decomposition into Low-rank plus Additive Matrices for Background/Foreground Separation: A Review for a Comparative Evaluation with a Large-Scale Dataset (2015, methodology): “[256][428][255] offer very impressive performance that are largely comparable to SubSENSE [354] and better than SOBS [253], ViBe [20] and PBAS [152][176].”
Background Subtraction in Real Applications: Challenges, Current Models and Future Directions (2019, methodology): “But, in practice, most of the authors in real applications employed basic techniques (temporal median [326][156][217], temporal histogram [422][201][406][393][238] and filter [184][193][38][248][67]) and relative old techniques like MOG [341] published in 1999, codebook [187] in 2004 and ViBe [28] i”
Traditional and recent approaches in background modeling for foreground detection: An overview (2014, methodology): “ViBe [21] and PBAS [124] are the leading methods in the ChangeDetection.”
ViBe: A Universal Background Subtraction Algorithm for Video Sequences (2011, methodology): “This method has been briefly described in [1] and in a patent [2].”