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Generalized Stauffer–Grimson background subtraction for dynamic scenes

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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.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of California, San Diego, 9500 Gilman Drive, Mail code 0409, La Jolla, CA, 92093-0409, USA

    Antoni B. Chan, Vijay Mahadevan & Nuno Vasconcelos

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  1. Antoni B. Chan
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  2. Vijay Mahadevan
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  3. Nuno Vasconcelos
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Correspondence to Antoni B. Chan.

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Chan, A.B., Mahadevan, V. & Vasconcelos, N. Generalized Stauffer–Grimson background subtraction for dynamic scenes. Machine Vision and Applications 22, 751–766 (2011). https://doi.org/10.1007/s00138-010-0262-3

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  • DOI: https://doi.org/10.1007/s00138-010-0262-3

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