Abstract

Background subtraction is an essential step in the process
of monitoring videos. Several works have been proposed to dierentiate
the background pixels from the foreground ones. Mixtures of Gaussian
(GMM) are among the most popular models for this problem. However,
they suer from some inconveniences related to light variations
and complex scene. This paper proposes an improvement of the GMM
by proposing a new technique of ordering the Gaussians distributions
in the selection phase of the scene\'s best representatives. This approach
replaces the usual ranking of Gaussian according to the value of wk;t=t
with sorting according to their covariance measure which is calculated
between each pixel and each of these Gaussians. The obtained results
on the Wallflower dataset has proven the eectiveness of the proposed
approach compared to standard GMM.

Information

Item Type:	Journal
Divisions:
ePrint ID:	4426
Date Deposited:	2023-09-17
Further Information:	Google Scholar
URI:	https://www.univ-soukahras.dz/en/publication/article/4426

BibTex