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Viola and Jones Object Detector

Viola and Jones Object Detector. Ruxandra Paun EE/CS/CNS 148 - Presentation 04.28.2005. Fast!. 15 times faster than any previous approach 384 by 288 pixel images detected at 15 frames per second on a conventional 700 MHz Intel Pentium III. 3 key contributors:

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Viola and Jones Object Detector

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  1. Viola and Jones Object Detector Ruxandra Paun EE/CS/CNS 148 - Presentation 04.28.2005

  2. Fast! • 15 times faster than any previous approach • 384 by 288 pixel images detected at 15 frames per second on a conventional 700 MHz Intel Pentium III

  3. 3 key contributors: - a new image representation: the “Integral Image” - a simple and effective classifier, based on the AdaBoost learning algorithm - combining the classifiers in a “cascade” Robust Real-Time Face Detection

  4. Detection basis: Features

  5. Integral Image

  6. Computing features

  7. Classifier: using AdaBoost • 160,000 features for every sub-window • Very small number of these features can be combined to form an effective classifier • AdaBoost: constrain each week classifier to depend on a single feature • each stage of boosting = new week classifier selection = feature selection

  8. First and Second Features Selected by AdaBoost

  9. ROC curve for a 200 feature classifier

  10. The Cascade • combining successively more complex classifiers in a cascade structure • 38 stages

  11. ROC curves: cascaded vs. monolithic classifier -> not significantly different accuracy -> but the cascade class. almost 10 times faster

  12. Results

  13. Training dataset: 4916 images

  14. ROC Curves for Face Detection

  15. Comparing Viola-Jones with Other Systems

  16. More: Detecting Walking Pedestrians • Integrating image intensity with motion information • Efficient, detects pedestrians at small scales, and has a very low false positive rate • Works on low resolution images and under difficult weather conditions (rain, snow)

  17. Extracting motion information

  18. Training Set Samples

  19. Questions?

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