BMOG: Boosted Gaussian Mixture Model with Controlled Complexity

• Type: Article in International Conference Proceedings Book
• Date: 2017

Authors

Martins, I (Author) Other; Pedro Carvalho (Author) Other; Luís Corte-Real (Author) FEUP; Luis Alba Castro, JL (Author) Other

Conference proceedings International

Title: PATTERN RECOGNITION AND IMAGE ANALYSIS (IBPRIA 2017)

Pages: 50-57

8th Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA)

Univ Algarve, Faro, PORTUGAL, JUN 20-23, 2017

Indexing

ISI Web of Knowledge - 11 Citations

Scopus - 18 Citations

Other information

Authenticus ID: P-00N-9XM

DOI: 10.1007/978-3-319-58838-4_6

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. The best solutions 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, BMOG, that significantly boosts the performance of the widely used MOG2 method. The complexity of BMOG is kept low, proving its suitability for real-time applications. 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 8