Rapid Object Detection using a Boosted Cascade of Simple Features Paul Viola, Michael Jones Conference 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

Rectangular Sum RectangularSum Location A1 B2-1 C3-1 D4+1-(2+3)

Learning Classification Function

Very small number of features can form an effective classifier Select best classifier feature Weak classifier

AdaBoost algorithm

Learning Result A frontal face classifier features (among 180,000) - Detection rate: 95% - False positive rate: 1/ s 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 Tradeoffs o Features↑ ↔ detection rates ↑ o Features↑ ↔ computational time ↓ Constructing stages o Training classifiers using AdaBoost o Adjust the threshold to minimize false negative

Result

Result Face training set o 4916 faces image o 24*24 pixels o 9544 image o 350 million sub-windows The complete face detection cascade has o 38 stages o 6061 features o 15 times faster than current system

Performance

Performance

Result

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