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).
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