Résumé

The diversity in background scenes such as, illumination changes, dynamics of the background, camouflage effect, shadow, etc. is a big deal for moving objects detection methods makes it impossible to manage the multimodality of scenes in video surveillance systems. In this
paper we present a new method that allows better detection of moving
objects. This method combine the robustness of the Articial Immune
Recognition System (AIRS) with respect to the local variations and the
power of Gaussian mixtures (MoG) to model changes at the pixel level.
The task of the AIRS is to generate several MoG models for each pixel.
This models are ltred through two mecanism: the competition for resources
and the development of a candidate memory cell. The best model
is merged with the exesting MoG according to the Memory cell introduction
process. Obtained results on the Wallfower dataset proved the performance of our system compared to other state-of-the-art methods.

Information

Item Type:	Journal
Divisions:
ePrint ID:	4428
Date Deposited:	2023-09-17
Further Information:	Google Scholar
URI:	https://www.univ-soukahras.dz/fr/publication/article/4428

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