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

• Tipo: Artigo em Revista Científica Internacional
• Data: 2018

Autores

Isabel Martins (Autor) Outra; Pedro Carvalho (Autor) Outra; Luís Corte-Real (Autor) FEUP; José Luis Alba‑Castro (Autor) Outra

Revista

Título: Pattern Analysis and Applications

Vol. 21 Nº 3

Páginas: 641-654

ISSN: 1433-7541

Editora: Springer Nature

Indexação

ISI Web of Knowledge - 1 Citação

Scopus - 7 Citações

Outras Informações

ID Authenticus: P-00N-S8M

DOI: 10.1007/s10044-018-0699-y

Abstract (EN): 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 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 14