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Rapid Object Detection using a Boosted Cascade of Simple Features
Paul Viola, Michael JonesConference on Computer Vision and
Pattern Recognition 2001 ( CVPR 2001 )
Outline• Introduction• Features• Learning Classification Functions• The Attentional Cascade• Result
Introduction
Three Contribution• New image representation - Integral image• Method for constructing a classifier - Selecting a small number of important features using AdaBoost• Method for combining classifiers - In a cascade structure
Features
Three Kind of Features
• Two-rectangle
• Three-rectangle
• Four-rectangle
• Feature value = sum of pixel value in white area - sum of pixel value in black area
Integral Image• Integral Image
Rectangular Sum
Rectangular Sum Location
A 1
B 2-1
C 3-1
D 4+1-(2+3)
Learning Classification
Function
Learning Classification
Function• Very small number of features can form an
effective classifier• Select best classifier feature• Weak classifier
AdaBoost algorithm
AdaBoost algorithm
Learning Result• A frontal face classifier - 200 features (among 180,000) - Detection rate: 95% - False positive rate: 1/14084 - 0.7s to scan an 384*288 pixel image
• First feature selected - The eyes is often darker than the nose and cheeks• Second feature selected - The eyes are darker than the bridge of the nose
The Attentional Cascade
Cascade
Training a cascade of classifiers
• Tradeoffso Features↑ ↔ detection rates ↑o Features↑ ↔ computational time ↓
• Constructing stageso Training classifiers using AdaBoosto Adjust the threshold to minimize false negative
Result
Result• Face training set
o 4916 faces imageo 24*24 pixelso 9544 image o 350 million sub-windows
• The complete face detection cascade haso 38 stageso 6061 featureso 15 times faster than current system
Performance
Performance
Result
Thank you for your attention!