improved adaptive gaussian mixture model for background zoran zivkovic pattern recognition, 2004....
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Improved Adaptive Gaussian Mixture Model for Background
Zoran Zivkovic
Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on
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Outline
Introduction Gaussian Mixture Model Select the number of components Experiments Conclusion
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Introduction Background subtraction is the common
process for surveillance system
Gaussian mixture model (GMM) was proposed for background subtraction Like Gaussian Dist-s model
These GMM-s use a fixed number of components
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Gaussian Mixture Model
are the estimate of the means are the estimate of the variance are mixing weight (non-negative an
d add up to one)
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Gaussian Mixture Model
Update equation a
a
a
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Gaussian Mixture Model
If the current pixel didn’t match with any distributions s
Decide pixel is in background/foreground d
sd
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Select the number of components
Goal choose the proper number of component
Implement Use prior and likelihood to select
proper models for given data
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Select the nmber of components Maximum Likelihood (ML)
Likelihood function:
Assume we have t data samples
a
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Select the number of components
Maximum Likelihood (ML) a
a
Constraint: weights sum up to one
The prior update func.
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Select the number of components
Dirichlet prior a presents the prior evidence for the cla
ss m – the number of samples that belong to that class a priori
Use negative coefficients means that accept class-m exist only if there i
s enough evidence from the data for the existence of this class
Cm
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Select the number of components
Maximum Likelihood (ML) +Dirichlet prior a
a
Fixed Expect a few components M and is small
a
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Experiments
New GMM with slight improvement
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Experiments
Max 4 Dist.
1 Dist.
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Experiments
In highly dynamic ‘tree’, the processing time is almost the same
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Conclusion
Present an improved GMM background subtraction scheme
The new algorithm can select the needed number of component
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The method of Stauffer and Grimson
is the learning rate that is defined by usesr