Generalized Stauffer-Grimson background subtraction for dynamic scenes

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

61 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)751-766
Journal / PublicationMachine Vision and Applications
Volume22
Issue number5
Publication statusPublished - Sept 2011
Externally publishedYes

Abstract

We propose an adaptive model for backgrounds containing significant stochastic motion (e.g. water). The new model is based on a generalization of the Stauffer-Grimson background model, where each mixture component is modeled as a dynamic texture. We derive an online K-means algorithm for updating the parameters using a set of sufficient statistics of the model. Finally, we report on experimental results, which show that the proposed background model both quantitatively and qualitatively outperforms state-of-the-art methods in scenes containing significant background motions. © 2010 Springer-Verlag.

Research Area(s)

  • Adaptive models, Background models, Background subtraction, Dynamic textures, Mixture models

Citation Format(s)

Generalized Stauffer-Grimson background subtraction for dynamic scenes. / Chan, Antoni B.; Mahadevan, Vijay; Vasconcelos, Nuno.
In: Machine Vision and Applications, Vol. 22, No. 5, 09.2011, p. 751-766.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review