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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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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 Phan, 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 Phan, 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 Phan, 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