Efficient local monitoring approach for the task of background subtraction. (September 2017)

• Record Type: Journal Article
• Authors: Farou, Brahim; Kouahla, Med Nadjib; Seridi, Hamid; Akdag, Herman
• Is Part Of: Engineering applications of artificial intelligence. Volume 64(2017:Apr.)
• Page Start: 1
• Page End: 12
• Publication Date: 2017-09
• Subjects: GMM -- Background subtraction -- Motion detection -- Machine vision -- Video surveillance -- Pearson's coefficient; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2017.05.013
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: We present in this paper a novel and efficient method that will significantly reduce GMM drawbacks in the presence of complex and dynamic scene. The main idea is to combine global and local features to remove local variations and the instant variations in the brightness that, in most cases, decrease the performance of background subtraction models. The first step is to divide the extracted frames into several equal size blocks. Then, we apply an adaptive local monitoring algorithm for each block to control local variation using Pearson similarity measurement. When a significant environment changes are detected in one or more blocks, the parameters of GMM assigned to these blocks are updated and the parameters of the rest remain the same. We also proposed merging adjacent and invariant blocks to reduce processing time and splitting the blocks that have an intense movement to improve accuracy. Experimental results on several datasets demonstrate that the proposed approach is effective and efficient under the common problems found in background modeling, outperforming the most referred state-of-the-art background subtraction methods.