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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: - PowerPoint PPT Presentation
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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
Robust Real-Time Face Detection 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”
Detection basis: Features
Integral Image
Computing features
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
First and Second Features Selected by AdaBoost
ROC curve for a 200 feature classifier
The Cascade combining successively more
complex classifiers in a cascade structure
38 stages
ROC curves: cascaded vs. monolithic classifier
-> not significantly different accuracy
-> but the cascade class. almost 10 times faster
Results
Training dataset: 4916 images
ROC Curves for Face Detection
Comparing Viola-Jones with Other Systems
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)
Extracting motion information
Training Set Samples
QuickTime™ and aYUV420 codec decompressor
are needed to see this picture.
Questions?