Background Subtraction Based on a Robust Consensus Method

Authors:
Hanzi Wang;David Suter
Affiliations:
Monash University, Clayton Vic. 3800, Australia;Monash University, Clayton Vic. 3800, Australia
Venue:
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Year:
2006

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Background subtraction for automated multisensor surveillance: a comprehensive review

EURASIP Journal on Advances in Signal Processing - Special issue on advanced image processing for defense and security applications

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Abstract

Statistical background modeling is a fundamental and important part of many visual tracking systems and of other computer vision applications. In this paper, we presents an effective and adaptive background modeling method for detecting foreground objects in both static and dynamic scenes. The proposed method computes SAmple CONsensus (SACON) of the background samples and estimates a statistical model per pixel. Numerous experiments on both indoor and outdoor video sequences show that the proposed method, compared with several state-of-theart methods, can achieve very promising performance.