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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.<\/p>","DOI":"10.4018\/ijsita.2019040102","type":"journal-article","created":{"date-parts":[[2019,11,7]],"date-time":"2019-11-07T07:31:26Z","timestamp":1573111886000},"page":"21-43","source":"Crossref","is-referenced-by-count":1,"title":["Using Resources Competition and Memory Cell Development to Select the Best GMM for Background Subtraction"],"prefix":"10.4018","volume":"10","author":[{"given":"Wafa","family":"Nebili","sequence":"first","affiliation":[{"name":"University 8 Mai 1945 Guelma, Guelma, Algeria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1609-6006","authenticated-orcid":true,"given":"Brahim","family":"Farou","sequence":"additional","affiliation":[{"name":"University 8 Mai 1945 Guelma, Guelma, Algeria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hamid","family":"Seridi","sequence":"additional","affiliation":[{"name":"LabSTIC laboratory, University 8 mai 1945 Guelma, Guelma, Algeria"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","reference":[{"key":"IJSITA.2019040102-0","doi-asserted-by":"crossref","unstructured":"Allebosch, G., Van Hamme, D., Deboeverie, F., Veelaert, P., & Philips, W. 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