Background Subtraction with Dirichlet Process Mixture Models

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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.
Original languageEnglish
Pages (from-to)670-683
Number of pages14
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Issue number4
Publication statusPublished - 5 Dec 2013


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