Background modeling for foreground detection in real-world dynamic scenes

Thierry Bouwmans, Jordi Gonzàlez, Caifeng Shan, Massimo Piccardi, Larry Davis

Research output: Contribution to journalReview articleResearchpeer-review

17 Citations (Scopus)


The 2014 Special Issue of Machine Vision and Applications discuss papers on the background modeling for foreground detection in real-world dynamic scenes. Shah and co-researchers adopt the mixture of Gaussians (MOG) as the basic framework for their complete system. A new online and self-adaptive method permits an automatic selection of the parameters for the GMM. Shimada and co-researchers propose a novel framework for the GMM to reduce the memory requirement without loss of accuracy. This 'case-based background modeling' creates or removes a background model only when necessary. Alvar and co-researchers present an algorithm called mixture of merged Gaussian algorithm (MMGA) to reduce drastically the execution time to reach real-time implementation, without altering the reliability and accuracy. Hagege describes a scene appearance model as a function of the behavior of static illumination sources, within or beyond the scene, and arbitrary three-dimensional configurations of patches and their reflectance distributions.
Original languageEnglish
Pages (from-to)1101-1103
JournalMachine Vision and Applications (Q2:Computer Vision and Pattern Recognition)
Issue number5
Publication statusPublished - 1 Jan 2014


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