[en] Background subtraction is a crucial step in many automatic video content analysis applications. While numerous acceptable techniques have been proposed so far for background extraction, there is still a need to produce more efficient algorithms in terms of adaptability to multiple environments, noise resilience, and computation efficiency. In this paper, we present a powerful method for background extraction that improves in accuracy and reduces the computational load. The main innovation concerns the use of a random policy to select values to build a samples-based estimation of the background. To our knowledge, it is the first time that a random aggregation is used in the field of background extraction. In addition, we propose a novel policy that propagates information between neighboring pixels of an image. Experiment detailed in this paper show how our method improves on other widely used techniques, and how it outperforms these techniques for
noisy images.
Research Center/Unit :
Intelsig Telim
Disciplines :
Electrical & electronics engineering
Author, co-author :
Barnich, Olivier ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Techniques du son et de l'image
Van Droogenbroeck, Marc ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Télécommunications
Language :
English
Title :
ViBe: a powerful random technique to estimate the background in video sequences
Publication date :
April 2009
Event name :
ViBe: a powerful random technique to estimate the background in video sequences
Event organizer :
IEEE
Event place :
Taipei, Taiwan
Event date :
04-2009
Audience :
International
Main work title :
International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2009)
Pages :
945-948
Peer reviewed :
Peer reviewed
Name of the research project :
Auralias
Funders :
DGTRE - Région wallonne. Direction générale des Technologies, de la Recherche et de l'Énergie
Commentary :
Get a Windows/Linux program or library to test ViBe on your own. Direct link to downloading page: http://www2.ulg.ac.be/telecom/research/vibe/download.html
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Bibliography
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C. Stauffer and E. Grimson, "Learning patterns of activity using real-time tracking," IEEE Trans. Pattern Anal. Mach. Intell., vol. 22, no. 8, pp. 747-757, 2000.
A. Elgammal, D. Harwood, and L. Davis, "Non-parametric model for background subtraction," in ECCV '00: Proceedings of the 6th European Conference on Computer Vision-Part II, London, UK, 2000, pp. 751-767, Springer-Verlag.
Z. Zivkovic and F. van der Heijden, "Efficient adaptive density estimation per image pixel for the task of background subtraction," Pattern Recognition Letters, vol. 27, no. 7, pp. 773-780, 2006.
H. Wang and D. Suter, "A consensus-based method for tracking: Modelling background scenario and foreground appearance," Pattern Recognition, vol. 40, no. 3, pp. 1091-1105, 2007.
Z. Zivkovic, "Improved adaptive gausian mixture model for background subtraction," in Proceedings of the International Conference on Pattern Recognition, 2004, pp. 28-31.
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