1. Background Subtraction with DirichletProcess Mixture Models.
- Author
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Haines, Tom S.F. and Xiang, Tao
- Subjects
- *
HIDDEN Markov models , *EMPIRICAL Bayes methods , *DATA modeling , *KERNEL (Mathematics) , *DIRICHLET forms , *ALGORITHMS - Abstract
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. [ABSTRACT FROM PUBLISHER]
- Published
- 2014
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