Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/55706
Type: Report
Title: A re-evaluation of mixture-of-gaussian background modeling
Author: Wang, Hanzi
Suter, David
Publisher: Monash University
Issue Date: 2004
Series/Report no.: Technical Report; MECSE-8-2004
School/Discipline: School of Computer Science
Statement of
Responsibility: 
Hanzi Wang and David Suter
Abstract: Mixture of Gaussians (MOG) has been widely used for robustly modeling complicated backgrounds, especially those with small repetitive movements (such as leaves, bushes, rotating fan, ocean waves, rain). The performance of MOG can be greatly improved by tackling several practical issues. In this paper, we quantitatively evaluate (using the Wallflower benchmarks) the performance of the MOG with and without our modifications. The experimental results show that the MOG, with our modifications, can achieve much better results - even outperforming other state-of-the-art methods.
RMID: 0020094177
Appears in Collections:Computer Science publications

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