A re-evaluation of mixture-of-gaussian background modeling
Date
2004
Authors
Wang, Hanzi
Suter, David
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Advisors
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Type:
Report
Citation
Statement of Responsibility
Hanzi Wang and David Suter
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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.
School/Discipline
School of Computer Science