Abstract

Background subtraction is an essential step in the video monitoring process. Several models
have been proposed to differentiate background pixels from foreground pixels. However,
most of these methods fail to distinguish them in highly dynamic environments. In this
paper, we propose a new method robust andmore efficient for distinguishingmoving objects
from static objects in dynamic scenes. For this purpose, we propose to use a bio-inspired
approach based on the Artificial Immune Recognition System (AIRS) as a classification
tool. AIRS separates antibodies, represented by the pixels of the background model, from
the antigens that model foreground pixels representing moving objects. Each pixel is modeled
by a feature vector containing the attributes of a Gaussian. Only the pixels classified
as background are taken into account by the system and updated in the model. This combination
has allowed to benefit from two advantages: the power of AIRS to provide an online
update of system parameters and the ability of Gaussians to adapt to scene variations at the
pixel level. To test the proposed approach, six videos representing the dynamic background
category of the CDnet 2014 dataset are selected. Obtained results proved the effectiveness
of this new process in terms of quality and complexity compared to other state-of-the-art
methods.

Information

Item Type:	Journal
Divisions:
ePrint ID:	4424
Date Deposited:	2023-09-17
Further Information:	Google Scholar
URI:	https://www.univ-soukahras.dz/en/publication/article/4424

BibTex