IMPROVED FACE TRACKING THANKS TO LOCAL FEATURES CORRESPONDENCE

IMPROVED FACE TRACKING THANKS TO LOCAL FEATURES CORRESPONDENCE

Alberto Piacenza, Fabrizio Guerrini,
RiccardoLeonardi

Department of Engineering Information
–

University of Brescia, Italy

Dataset

of

YouTube

shots

with

faces

(average
:

181

frames)

FACE TRACKING ENHANCEMENT

OVERVIEW

Apply the
face track

enhancement stage

SOLUTION

Semantic

description

of

the

content

in

the

Interactive

Movietelling

system

[
1
]

MOTIVATION

Identify the frames in which

a main character is present

SPECIFIC CHALLENGE

Use off
-
the
-
shelf tools for:

1)
face detection

2)
face recognition on the
detected faces

BASELINE SOLUTION

1)
Imprecise or missed
face detection

2)
Face bounding box
drifting

PROBLEMS

Character recognition

i
s unreliable

EFFECTS

Flowchart

of

the

operations

involved

in

the

creation

of

the

enhanced

face

tracks
.

Output

tracks
:

the

frames

of

a

small

excerpt

of

one

shot

are

presented
.


Blue

rectangles
:

detected

faces

correctly

identified

in

a

given

face

track
.

but

the

face

detection

has

failed

to

find

the

face

in

the

in
-
between

frames
.


Green

rectangles
:

recovered

faces

for

in
-
between

frames

thanks

to

the

face

tracks

enhancement

process
.

•
Re
-
extract

POI

in

the

bounding

box

•
Use

KLT

tracker

to

the

next

frame

•
Estimate

the

new

bounding

box

using

RANSAC

•
Use

backward

tracking

as

well

: number of ground
-
truth objects in frame

: number of detected objects in frame

:
-
th

ground
-
truth object

:
-
th

detected object

Frame Detection Accuracy (FDA):

Comparison with the CAMSHIFT algorithm

EXPERIMENTAL RESULTS

ADDITIONAL INFO

Interactive
Movietelling

system:

•

Reference:


[1] A. Piacenza, F. Guerrini, N.
Adami
, R.
Leonardi
,


J.
Porteous
, J.
Teutenberg
, M.
Cavazza
, “Generating


Story Variants with Constrained Video Recombination”,

19
th

ACM Multimedia
, pp. 223
-
232, 2011.

•

Link to example output clips:

www.ing.unibs.it/alberto.piacenza/TrackWithPoints

Acknowledgements
: This work has been

funded (in part) by the EC under grant

Agreement IRIS (FP7
-
ICT
-
231824).