Pattern Analysis and Applications

, Volume 21, Issue 3, pp 641–654 | Cite as

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

  • Isabel Martins
  • Pedro Carvalho
  • Luís Corte-Real
  • José Luis Alba-Castro
Original Article


Developing 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 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.


GMM MOG Background subtraction Change detection Foreground segmentation Background model 



This work has received financial support from the Xunta de Galicia (Agrupación Estratéxica Consolidada de Galicia accreditation 2016–2019) and the European Union (European Regional Development Fund—ERDF) and research contract GRC2014/024 (Modalidade: Grupos de Referencia Competitiva 2014) and project “TEC4Growth—Pervasive Intelligence, Enhancers and Proofs of Concept with Industrial Impact/NORTE-01-0145-FEDER-000020,” financed by the North Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, and through the European Regional Development Fund (ERDF). It was also partially supported by MOG CLOUD SETUP—N17561, supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF).


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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.ISEP, School of EngineeringPolytechnic Institute of PortoPortoPortugal
  2. 2.Signal Theory and Communications DepartmentUniversity of Vigo36310 VigoSpain
  3. 3.INESC TECPortoPortugal
  4. 4.Faculty of EngineeringUniversity of PortoPortoPortugal

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