Generalized Stauffer–Grimson background subtraction for dynamic scenes

Authors:
Antoni B. Chan;Vijay Mahadevan;Nuno Vasconcelos
Affiliations:
University of California, San Diego, Department of Electrical and Computer Engineering, 9500 Gilman Drive, Mail code 0409, 92093-0409, La Jolla, CA, USA;University of California, San Diego, Department of Electrical and Computer Engineering, 9500 Gilman Drive, Mail code 0409, 92093-0409, La Jolla, CA, USA;University of California, San Diego, Department of Electrical and Computer Engineering, 9500 Gilman Drive, Mail code 0409, 92093-0409, La Jolla, CA, USA
Venue:
Machine Vision and Applications - Special Issue on Dynamic Textures in Video
Year:
2011

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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.