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 Conclusion

Outline

Introduction Features Learning classification functions The attentional cascade Result Conclusion

Introduction

New object detection framework Motive

Face recognition Characteristics

Robust Rapid

Contributions

1. New image representation Integral image

2. Method for constructing a classifier Selecting a small number of important features

using AdaBoost

3. Method for combining classifiers in a cascade structure

Application

Rapid face detector can be used in User interfaces Image databases Teleconferencing

Especially, … Allow for post-processing

When rapid frame-rates are not necessary Can be implemented on small low power devices

Handhelds, embedded processors

Outline

Introduction Features Learning classification functions The attentional cascade Result Conclusion

Features

Why not pixels? The most common reason

Features can encode ad-hoc domain knowledge The critical reason for this system

Feature based system operates much faster 3 kind of features used

Two-rectangle feature Three-rectangle feature Four-rectangle feature

Integral Image

( x ,y )

( 0 ,0 )

integral image

original image

Rectangular sum

Rectangular sum Location

A 1

B 2-1

C 3-1

D 4+1-(2+3)

Outline

Introduction Features Learning classification functions The attentional cascade Result Conclusion

Learning classification functions Hypothesis

Very small number of features can form an effective classifier How to find

Select the single rectangle feature which best separates the positive and negative examples

Weak classifier

Result Features selected in early round

Error rate: 0.1~0.3 Features selected in later round

Error rate: 0.4~0.5

threshold

featurepolarity

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 Not sufficient

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

Outline

Introduction Features Learning classification functions The attentional cascade Result Conclusion

The attentional cascade Constructing goal Reject many of the negative sub-window Detect almost all positive instances

False negative rate → 0 Cascade

Training a cascade of classifiers Tradeoffs

Features↑ ↔ detection rates ↑ Features↑ ↔ computational time ↓

Constructing stages Training classifiers using AdaBoost Adjust the threshold to minimize false negative

Outline

Introduction Features Learning classification functions The attentional cascade Result Conclusion

Result Face training set

4916 hand labeled faces Resolution: 24*24 pixels Source: random crawl of the WWW 9544 manually inspected image 350 million sub-windows

The complete face detection cascade has 38 stages 6061 features 15 times faster than current system

Layer 1 2 3 4 5features 1 10 25 25 50

PerformanceReceiver operating characteristic (ROC)

What’s ROC? (please reference http://www.geocities.com/shinyuanclub/update97/lucm0115.html )

Performance comparison

Detection rates for various numbers of false positives on the MIT+CMU test set containing 130 images and 507faces

Outline

Introduction Features Learning classification functions The attentional cascade Result Conclusion

Conclusions

An approach for object detection Minimize computation time

15 times faster than any previous approach Achieve high detection accuracy

false negativefalse positive