A Re-evaluation of Mixture-of-Gaussian Background Modeling
Hanzi Wang, David Suter · 2006
The 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.