Background Subtraction with DirichletProcess Mixture Models

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 informat...

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Bibliographic Details
Published in:IEEE transactions on pattern analysis and machine intelligence 2014-04, Vol.36 (4), p.670-683
Main Authors: Haines, Tom S. F., Tao Xiang
Format: Article
Language:English
Subjects:
Background subtraction
Bayes methods
Computational modeling
confidence capping
Counting
Data models
Dirichlet problem
Dirichlet processes
Gaussian
Hidden Markov models
Image color analysis
Intelligence
Kernel
Learning
Noise
non-parametric Bayesian methods
Pattern analysis
Subtraction
Transaction processing
video analysis
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Summary: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.
ISSN:0162-8828
1939-3539
2160-9292
DOI:10.1109/TPAMI.2013.239