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Automatic Car Detection

Using Statistic-based Boosting Cascade

Weilong Yang, Wei Song, Zhigang Qiao, Michael Fang

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Haar-like feature generation◦ Integral image

AdaBoost (feature selection)◦ Limitations

Statistic-based boosting (StatBoost) Cascade structure Performance evaluation

System Overview

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Features:

Training Images Set:

500 negative samples (w/o cars)

500 positive samples (with cars)

Training Image size: 40X100 pixels

imresize(X, 0.35), resize ratio, 0.35

Feature Templates:

Rectangle features were used.

Total 9 types of features were used

Integral graph was used to accelerate the calculation of rectangle features.

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Features Templates:

Edge Features: (1) (2)

Line Features: (3) (4)

Diagonal Line Features:

(5)

Corner Features: (6) (7) (8) (9)

Integral Image

B D

A C

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3

2

1

(x ,y )

yyxx

yxiyxii','

)','(),(

The sum within D can be computed as: 4 + 1 − (2 + 3).

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Features in 14X35 windows

The feature templates can be laid at any position with any possible size in the 14X35 windows.

One possible position and size of some type of feature define a feature. All the possible features form the total feature set of the 14X35 windows.

Total 177660 features

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The objective is to learn a sequence of best weak classifiers and the best combining weights

Each weak classifier is constructed based on one feature (1D).

Key Concepts of AdaBoost

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AdaBoost (illustrated)

Weak classifier 3

Final classifier: linear combination of the weak classifiers

WeightsIncreased

Weak Classifier 2

Weak Classifier 1

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Formal Procedure of AdaBoost

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It takes WEEKS to train!

Computational Issue

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The Bottleneck

Factor

Description Common value

N number of examples

10,000

M number of weak classifiers in total

4,000 - 6,000

T number of Haar-like features

40,000

A view of a face detector training algorithm

for weak classifier m from 1 to M:…update weights – O(N)for feature t from 1 to T:

compute N feature values – O(N)sort N feature values – O(N log N)train feature classifier – O(N)

select best feature classifier – O(T)…

Training of the weak classifier

Figure courtesy to Minh-Tri Pham, 2007

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Train the weak classifiers using statistics Assume the feature values of each class are

of normal distribution

Fast-StatBoost (1)

Non-car

Car

Optimalthreshold

Featurevalue

Figure courtesy to Minh-Tri Pham, 2007

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Fast-StatBoost (2)

A view of our face detector training algorithm

for weak classifier m from 1 to M:…update weights – O(N)Extract statistics of integral image – O(Nd2)for feature t from 1 to T:

project statistics into 1D – O(1)train feature classifier – O(1)

select best feature classifier – O(T)…

Constant Time

Figure courtesy to Minh-Tri Pham, 2007

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Motivation◦ Increase detection performance◦ Reduce computation time

Key insight◦ Use simpler classifiers to reject the majority of

sub-windows◦ Use more complex ones to achieve low false

positive rates on the rest of them

The Cascade

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Overall form: degenerate decision tree Reflects the fact: within any single image an

overwhelming majority of sub-windows are negative [1]

The Cascade (illustrated)

All Sub-windows

1 2 3

Rejected Sub-windows

Further Processing

T T T

FF F

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False positive rate

Detection rate

A Cascade of Classifiers

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Three-layer cascade Each layer has 4, 11, and 73 weak

classifiers, respectively

Our Cascaded Car Detector

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Training Set (1)

Number of car images: 500

(Courtesy to UIUC Image Database for Car Detection)

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Training Set (2)

Number of non-car images: 500

(Courtesy to UIUC Image Database for Car Detection)

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First Three Features

1st Feature 2nd Feature

3rd Feature

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Distribution of the First Three Features

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Car Detection (Uniform Scale)

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Car Detection (Different Scales)

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False Positives

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The ROC curveTest results on 170 Images:

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References

1. P. Viola and M. Jones, “Rapid Object Detection Using a Boosted Cascade of Simple Features,” In Proc. CVPR, 2001.

2. M. Pham, T. Cham, “Fast training and selection of Haar features using statistics in boosting-based face detection.” In Proc. ICCV 2007