Robust Real-time Object Detection

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6 Νοε 2013 (πριν από 3 χρόνια και 11 μήνες)

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Robust

Real
-
time
Object

Detection

Paul Viola


Michael Jones





SECOND INTERNATIONAL WORKSHOP ON STATISTICAL
AND COMPUTANIONAL THEORIES OF VISION


MODELING, LEARNING, COMPUTING
AN
D

SAMPLING


VANCOUVER, CANADA, JULY 13, 2001.

1

Aluna:

Lourdes Ramírez Cerna.

Introduction

Face

recognition

has

become

an

area

of

active

research
,

that

spans

disciplines

such

as

image

processing
,

pattern

recognition
,

computer

vision
,

neural

networks

and

so

on
.


The

first

step

in

a

face

recognition

system

is

the

face

detection
.

Given

an

image

or

video,

a

face

identifier

must

be

able

to

identify

and

locate

all

faces

regardless

their

position,

scale
,

age
,

orientarion

and

lighting

conditions
.

Scale

Orientation

L
ighting

conditions

2

3

The

Problem

There

are

hundred

detection

methods

in

the

literature,

but

many

of

them

don’t

work

in

real
-
time

so

the

method

proposed

by

Viola
-
Jones

was

the

first

real
-
time

robust

detection

system
.



This

paper

presents

new

algorithms

to

construct

a

framework

for

robust

and

extremely

rapid

object

detection,

which

achieves

detection

and

false

positive

rates

equivalent

to

the

best

published

results
.




4

Framework
Scheme

Consists

in

two

steps
:


1.
Trainer
:

works

with

positive

(objects

with

faces)

and

negative

(objects

without

faces)

samples
.

It’s

a

lengthy

process

to

be

calculated
.

2.
Detector
:

uses

the

trainer

detector

to

analyze

each

input

image
.

This

second

stage

is

very

fast

and

allows

real
-
time

detection
.




5

Features

The

object

detection

procedure

classifies

images

based

on

the

value

of

simple

features

called

Haar
-
like

Features
.


A

feature
-
based

system

operates

much

faster

than

a

pixel
-
based

system
.







6

Integral
Image

Rectangle

features

can

be

computed

very

rapidly

using

an

intermediate

representation

for

the

image

which

call

the

integral

image
.






Integral
Image

Calculate

rectangular
feature

7

Training
The

Attentional

Cascade

8

9

10

11

Detection

12

Experiments


The

positive

training

set

consisted

of

4916

hand

labeled

faces

scaled

and

aligned

to

a

base

resolution

of

24

by

24

pixels
.


And

10

000

negative

set

examples

were

selected

by

randomly

picking

sub

windows

from

9500

images

which

didn’t

contain

faces
.





13


The

speed

of

the

cascaded

detector

is

directly

related

to

the

number

of

features

evaluated

per

scanned

sub

window
.


The

final

classifier

had

32

layers

and

4297

features

total
.


Evaluated

on

the

MIT
-
CMU

test

set

an

average

of

8

features

out

of

a

total

of

4297

are

evaluated

per

sub
-
window
.


The

processing

time

of

a

384

by

288

pixel

image

on

a

conventional

personal

computer

about

0
.
067

seconds
.


14

Results

Testing

of

the

final

face

detector

was

performed

using

the

MIT

CMU

frontal

face

test

which

consists

of
:



130

images



507

frontal

faces
.

15