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Improved Adaptive Gaussian Mixture Model for Background

Improved Adaptive Gaussian Mixture Model for Background. Zoran Zivkovic Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on. Outline. Introduction Gaussian Mixture Model Select the number of components Experiments Conclusion. Introduction.

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Improved Adaptive Gaussian Mixture Model for Background

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  1. Improved Adaptive Gaussian Mixture Model for Background Zoran Zivkovic Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on

  2. Outline • Introduction • Gaussian Mixture Model • Select the number of components • Experiments • Conclusion

  3. 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

  4. Gaussian Mixture Model • are the estimate of the means • are the estimate of the variance • are mixing weight (non-negative and add up to one)

  5. Gaussian Mixture Model • Update equation • a • a • a

  6. Gaussian Mixture Model • If the current pixel didn’t match with any distributions • s • Decide pixel is in background/foreground • d • sd

  7. Select the number of components • Goal choose the proper number of component • Implement • Use prior and likelihood to select proper models for given data

  8. Select the nmber of components • Maximum Likelihood (ML) • Likelihood function: • Assume we have t data samples • a

  9. Select the number of components • Maximum Likelihood (ML) • a • a Constraint: weights sum up to one The prior update func.

  10. Select the number of components • Dirichlet prior • a • presents the prior evidence for the class m – the number of samples that belong to that class a priori • Use negative coefficients • means that accept class-m exist only if there is enough evidence from the data for the existence of this class

  11. Select the number of components • Maximum Likelihood (ML) +Dirichlet prior • a • a • Fixed • Expect a few components M and is small • a

  12. Experiments New GMM with slight improvement

  13. Experiments 1 Dist. Max 4 Dist.

  14. Experiments In highly dynamic ‘tree’, the processing time is almost the same

  15. Conclusion • Present an improved GMM background subtraction scheme • The new algorithm can select the needed number of component

  16. The method of Stauffer and Grimson • is the learning rate that is defined by usesr

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