BMOG: boosted Gaussian Mixture Model with controlled complexity for background subtraction

TitleBMOG: boosted Gaussian Mixture Model with controlled complexity for background subtraction
Publication TypeJournal Article
Year of Publication2018
AuthorsMartins, I, Carvalho,, -Real, C, Alba-Castro, JL
JournalPattern Analysis and Applications
Volume21
Issue3
Start Page641
AbstractDeveloping robust and universal methods for unsupervised segmentation of moving objects in video sequences has proved to be a hard and challenging task that has attracted the attention of many researchers over the last decades. State-of-the-art methods are, in general, computationally heavy preventing their use in real-time applications. This research addresses this problem by proposing a robust and computationally efficient method, coined BMOG, that significantly boosts the performance of a widely used method based on a Mixture of Gaussians. The proposed solution explores a novel classification mechanism that combines color space discrimination capabilities with hysteresis and a dynamic learning rate for background model update. The complexity of BMOG is kept low, proving its suitability for real-time applications. BMOG was objectively evaluated using the ChangeDetection.net 2014 benchmark. An exhaustive set of experiments was conducted, and a detailed analysis of the results, using two complementary types of metrics, revealed that BMOG achieves an excellent compromise in performance versus complexity.
DOI10.1007/s10044-018-0699-y
Citation Key644