Abstract

Background subtraction is an essential step in the process of monitoring videos. Several works have
proposed models to differentiate the background pixels from the foreground pixels. Mixtures of
Gaussian (GMM) are among the most popular models for a such problem. However, the use of a
fixed number of Gaussians influence on their results quality. This article proposes an improvement
of the GMM based on the use of the artificial immune recognition system (AIRS) to generate and
introduce new Gaussians instead of using a fixed number of Gaussians. The proposed approach
exploits the robustness of the mutation function in the generation phase of the new ARBs to create
new Gaussians. These Gaussians are then filtered into the resource competition phase in order to keep
only ones that best represent the background. The system tested on Wallflower and UCSD datasets
has proven its effectiveness against other state-of-art methods.

Information

Item Type:	Journal
Divisions:
ePrint ID:	4427
Date Deposited:	2023-09-17
Further Information:	Google Scholar
URI:	https://www.univ-soukahras.dz/en/publication/article/4427

BibTex