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Background Subtraction with DirichletProcess Mixture Models

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2 Author(s)
Haines, T.S.F. ; Dept. of Comput. Sci., Univ. Coll. London, London, UK ; Tao Xiang

Video analysis often begins with background subtraction. This problem is often approached in two steps-a background model followed by a regularisation scheme. A model of the background allows it to be distinguished on a per-pixel basis from the foreground, whilst the regularisation combines information from adjacent pixels. We present a new method based on Dirichlet process Gaussian mixture models, which are used to estimate per-pixel background distributions. It is followed by probabilistic regularisation. Using a non-parametric Bayesian method allows per-pixel mode counts to be automatically inferred, avoiding over-/under- fitting. We also develop novel model learning algorithms for continuous update of the model in a principled fashion as the scene changes. These key advantages enable us to outperform the state-of-the-art alternatives on four benchmarks.

Published in:

Pattern Analysis and Machine Intelligence, IEEE Transactions on  (Volume:36 ,  Issue: 4 )