Real-Time Object Detection

Robust Real-Time Object DetectionAuthor: Paul Viola, Michael J. Jones
International Journal of Computer Vision 57(2), 137–154, 2004
Presented by ERLI – M10115801

Background & Motivation
• Detecting object on real time
• Detecting face

Outline
Introduction
Image Representation – Integral Image
Learning Algorithm – AdaBoost
Cascade Classifier
Experiment and Result

Introduction
Object Detection
Detection System

Object Detection
>> Face Detection
Paul Viola Michael J. Jones
Introduction

Detection System
24x24
Detection
Window
The size of
detection window
will be enlarged
with a certain scale
Introduction

Image
Representation
Integral Image
Integral Image Feature

Integral Image
• Image representation
• Feature
• The integral image at location x,y
contains the sum of the pixels
above and to the left of x,y
Integral Image | AdaBoost | Cascade
Sum of pixel
value in this area
(x,y)

Integral Image
• Feature
• The integral image at location x,y
contains the sum of the pixels
above and to the left of x,y
Sum of pixel
value in this area
(x,y)
How to calculate D ?
A B
C D
1 2
3 4

Integral Image
• Feature
How to calculate D ?
D = (1+4) – (2+3)
A B
C D
1 2
3 4
1
2
3
4

Detection window
A
B
C
D
Detection window
Detection window Detection window
The value of integral image
feature is the difference between
the sum of the pixels within the
two rectangular regions.
S1 S2 |S1-S2|

The size and position of feature can be
different.
• The eye region is darker
than the upper-cheeks.
• The nose bridge region is
brighter than the eyes.

Integral Image
• Different size and position of integral image give so many possible
features
• How do we obtain the best representing features possible?
• How can we refrain from wasting time on image background? (i.e.
non-object)
Good Feature Not Good Feature
AdaBoost…

AdaBoost
Weak Classifier
Strong Classifier
AdaBoost

Weak Classifier
• Consist of just 1 feature
• 1 feature that can separate the image data with error rate less than 0.5
Feature 1
Feature 2
Feature 3
θ 1
θ 2
θ 3
Weak
Classifier 1
Weak
Classifier 2
Weak
Classifier 3
1/10 = 0.1
6/10 = 0.6
3/10 = 0.3

Strong Classifier
• Set of features/weak classifier
1 1 1
strong
1
1 ( ) ( )
( ) 2
0 otherwise
n n nh h
h
x x
x
 
Weak Classifier
How to build the strong classifier?
AdaBoost

AdaBoost
Pseudo Code
• T = number of feature we choose
• h(x, f,p,θ)) = A weak classifier
• f = feature
• θ = threshold
• p = polarity indicating the direction of the
inequality (less than or greater than θ)

 AdaBoost starts with a uniform distribution
of “weights” over training examples.
 Select the classifier with the lowest weighted
error (i.e. a “weak” classifier)
 Increase the weights on the training
examples that were misclassified.
 (Repeat)
 At the end, carefully make a linear
combination of the weak classifiers obtained
at all iterations.
AdaBoost Ilustration
1 1 1
strong
1
1 ( ) ( )
( ) 2
0 otherwise
n n nh h
h
x x
x
 
Slide taken from a presentation by Qing Chen, Discover Lab, University of Ottawa

Classifier
• By increasing the number of features per classifier, we:
◦ Increase detection accuracy.
◦ Decrease detection speed.
• Experiments showed that a 200 feature classifier
makes a good face detector:
◦ Takes 0.7 seconds to scan an 384 by 288 pixel image.
• Problem: Not real time! (At most 0.067 seconds
needed).
Cascade of Classifier…

Cascade of Classifier
• Classifier is structured as several layer/hierarchy
• Each layer consist of 1 strong classifier (set of weak
classifiers/features)
• Evaluating all input window
• Ignore non-face window
• Continue for processing “suspected” face window

• The number of feature/weak classifier in every layer is based on the
value of false positive rate and detection rate determined by user
• Previous layers have simpler classifier than next layer
Stage 1 Stage 2 Stage 3 …
Further
Processing
Input Image
Window
Ignore/Reject
Input
Ignore/Reject
Input
Ignore/Reject
Input
Ignore/Reject
Input
Y
N
Y
N
Y
N
Y
N

Stage 1
(2 feature)
N
Y Stage 2
(2++ feature)

Cascade of
Classifier
Pseudo Code

Experiment
• Training set
• Face: 4916 labeled frontal faces
•Testing / Real-time test
• Face: MIT + CMU frontal face test set
• Contain: 130 images with 507 labeled frontal faces

Result
Cascade Classifier
v.s
Non Cascade Classifier
Cascade classifier
nearly 10x faster.

Result
They use dataset MIT+CMU with 5 images removed
Voting (Using 3 Viola-Jones Classifier with different parameter)
++- : Face
--+ : Non Face

Failure Mode
• Informal observation suggests that the face detector can detect
faces that are tilted up to about ±15 degrees in plane and about ± 45
degrees out of plane
• Harsh backlighting in which the faces are very dark while the
background is relatively light sometimes causes failures
• Proposed algorithm will also fail on significantly occluded faces

Real-Time Object Detection

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