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Background Estimation with Gaussian Distribution for Image Segmentation, a fast approach

Background Estimation with Gaussian Distribution for Image Segmentation, a fast approach. Gianluca Bailo, Massimo Bariani, Paivi Ijas, Marco Raggio IEEE International Workshop on Measurement Systems for Homeland Security, Contraband Detection and Personal Safely. Outline. Introduction

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Background Estimation with Gaussian Distribution for Image Segmentation, a fast approach

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  1. Background Estimation with Gaussian Distribution for Image Segmentation, a fast approach Gianluca Bailo, Massimo Bariani, Paivi Ijas, Marco Raggio IEEE International Workshop on Measurement Systems for Homeland Security, Contraband Detection and Personal Safely

  2. Outline • Introduction • The method of Stauffer and Grimson • Improvement of the method of Stauffer and Grimson • Results • Conclusion

  3. Introduction • Concentrate in the speed of the algorithm with active pixels instead of all the pixels • Using Gaussian distributions to model the history of active pixels • According to the classification of a part of the background or foreground

  4. Previous Work – background model • Simplesta constant model • Problem: illumination, object added to or removed to from the background, shadows, repetitive motion • Illumination: use Kalman filtering to update the background model • Shadows: only for a particular scene for vehicle detection Must adapt the background !!

  5. Previous Work – background model • Repetitive motion & different lightning condition: suffer from slow learning of the background at the beginning and not very fast • Lightning, repetitive motion, object added or removed: solve many problems in pixel, region, frame levels, but speed of algorithm is questioned

  6. The method of Stauffer and Grimson • Modelling pixel history with K Gaussian probability density distributions • The history of a certain pixel is be defined as a time series • is a vector in color image scalar in grey level image

  7. The method of Stauffer and Grimson • The probability to observe a certain pixel value within the history values of the pixel is • is the weight parameter that is used to describe by the Gaussian distribution • is a Gaussian distribution that has two parameters: is the mean of the Gaussian distribution at time t and is the covariance matrix at time instant t

  8. The method of Stauffer and Grimson • A new pixel is said to match a distribution • If it is within 2.5 standard deviations from mean of the distribution • This distribution are updated with this pixel value

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

  10. The method of Stauffer and Grimson • The weight parameters of all distributions are updated as • is 1, for matched distribution 0, for unmatched distribution

  11. The method of Stauffer and Grimson • If the current pixel didn’t match with any of the K distributions • The distribution with smallest weight is replaced by a new distribution • The new distribution with • The mean is set to the value of the current pixel • The variance is set large • The weight is set to a small value

  12. The method of Stauffer and Grimson • Define which of distributions describing the history of a pixel result from background • 1. Order all distributions by a factor • 2. B first distributions are marked as background distribution • If a pixel is matched with one of these B distributions it is marked as a background pixel

  13. Improvement of the method of Stauffer and Grimson • Grey-scale image: not using color images as Stauffer and Grimson did • Active/inactive pixels • The values of neighboring pixels have a correlation • Every other/third pixel is set as active pixel • Only active pixels are modelled with K Gaussian distributions

  14. Results • Video sequences with 25 frames per second • 3 Gaussian distributions were used • All tests are done using 1.7 GHz, Pentium 4, 256 MB RAM

  15. Results- method of Stauffer & Grimson

  16. Results-Improvement with every n-th pixel Original method Every 2 pixel is examined Every third pixel is examined

  17. Result - time • The times needed to elaborate one frame

  18. Result – noisy pixels

  19. Conclusions • The method of Stauffer and Grimson • Illumination – with distribution & update distribution • Repetitive motion – with different n distributions • New object add to background – with update distribution • Improvement method • Use every n-th pixel as active pixel to speed surveillance system

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