VIDEO PROCESSING IN MOTION MODELLING

Transkript

VIDEO PROCESSING
IN MOTION MODELLING
Miroslav KUBÍČEK, Aleš PROCHÁZKA, Aleš PAVELKA
[email protected], [email protected], [email protected]
Institute of Chemical Technology
Department of Computing and Control Engineering
Digital Signal and Image Processing Research Group
http://dsp.vscht.cz
VIDEO PROCESSING IN MOTION MODELLING – Miroslav KUBÍČEK, Aleš PROCHÁZKA, Aleš PAVELKA – Process Control 2005 – p.1/10
Contents
Introduction
Contents
Introduction
System Description
Contents
Introduction
System Description
Three-Dimensional Object Detection
Contents
Introduction
System Description
Motion Visualization
Contents
Introduction
System Description
Conclusions
Introduction
Goals of the project
Study of image acquisition using synchronized two
camera system and A/D convertors
Study of mathematical methods for body
localization in the three dimensional space
Visualization of the body movement using virtual
reality environment
Introduction
Goals of the project
Study of image acquisition using synchronized two
camera system and A/D convertors
Study of mathematical methods for body
localization in the three dimensional space
Visualization of the body movement using virtual
reality environment
Application
Modelling of the body movement
Analysis of the object movement
using several reference points
System Description
Light
@i
@
Technical details
Cameras with color CCD
sensor and with resolution
1024x768, 30 fps
A
Camera
B
Camera
Synchronization 125 µs
Connection with computer
via IEEE 1394
Direct connection to the
MATLAB system and Image
Acquisition Toolbox
IEEE 1394
?
?
MATLAB
&
Image Acquisition Tlbx
Computer
Principle of the object localisation
(b) K−TH POSITION
(a) INITIAL POSITIONING
FRONT VIEW
FRONT VIEW
C [xC(k),zC(k)]
b2(k)
C [x (1),0]
a (k)
2
α2(k)
C
β2(k)
TOP VIEW
TOP VIEW
C [x (1),y (1)]
C
C
C [xC(k),yC(k)]
b (1)
1
α1(1)
a1(1)
c
b (k)
1
β1(1)
α1(k)
a1(k)
c
β1(k)
A [xA,yA]
B [xB,yB]
A [xA,yA]
B [xB,yB]
Camera A
Camera B
Camera A
Camera B
Principle of the object localisation
(b) K−TH POSITION
(a) INITIAL POSITIONING
FRONT VIEW
FRONT VIEW
C [xC(k),zC(k)]
b2(k)
C [x (1),0]
a (k)
2
α2(k)
C
β2(k)
TOP VIEW
TOP VIEW
C [x (1),y (1)]
C
C
C [xC(k),yC(k)]
b (1)
1
α1(1)
a1(1)
c
b (k)
1
β1(1)
α1(k)
a1(k)
c
β1(k)
A [xA,yA]
B [xB,yB]
A [xA,yA]
B [xB,yB]
Camera A
Camera B
Camera A
Camera B
2
2
2
α1 (1) = arccos (b1 (1) + c − a1 (1) )/(2 b1 (1) c)
2
2
2
β1 (1) = arccos (a1 (1) + c − b1 (1) )/(2 a1 (1) c)
Calibration of the camera system
HORIZONTAL AND VERTICAL CAMERA ANGLE EVALUATION
(b) INITIAL LIGHT POSITIONING
(a) CALIBRATION GRID
α2max
α (1)
2
d
svertical/2
CAMERA
α
2min
d
shorizontal/2
α
1min
α1(1)
α
1max
Calibration of the camera system
HORIZONTAL AND VERTICAL CAMERA ANGLE EVALUATION
(b) INITIAL LIGHT POSITIONING
(a) CALIBRATION GRID
α2max
α (1)
2
d
svertical/2
CAMERA
α
2min
d
shorizontal/2
α
1min
α1(1)
α
1max
αhorizontal
αvertical
= 2 arctan shorizontal /2/d
= 2 arctan svertical /2/d
MOTION MODELLING
19
30
20
8
z−axis
25
20
18
15
21
2728
7119
10
23
22
17
1
10
16
5
3
15
2
0
26
24
25
4
29
1230
6
CAMERA B
−5
13
5
14
1500
1500
CAMERA A
1000
1000
500
y−axis
500
0
0
x−axis
MOTION MODELLING
19
30
20
8
z−axis
25
20
18
15
21
2728
7119
10
23
22
17
1
10
16
5
3
15
2
0
26
24
25
4
0
29
1230
6
CAMERA B
−5
13
5
Display
14
1500
1500
CAMERA A
1000
1000
500
y−axis
500
0
simin
0
Bally.translation
x−axis
From
Workspace
VR Sink
Scope
MOTION MODELLING
19
30
20
8
z−axis
25
20
18
15
21
2728
7119
10
23
22
17
1
10
16
5
3
15
2
0
26
24
25
4
0
29
1230
6
CAMERA B
−5
13
5
Display
14
1500
1500
CAMERA A
1000
1000
500
y−axis
500
0
simin
0
Bally.translation
x−axis
From
Workspace
VR Sink
Scope
Conclusions
Results
Conclusions
Results
Successfully tested system with one moving object
Conclusions
Results
Creation of the virtual reality model based on the
real movement
Conclusions
Results
real movement
Further Research
Conclusions
Results
real movement
Further Research
Deterministic and statistical analysis of the set of
moving objects
Conclusions
Results
real movement
Further Research
Deterministic and statistical analysis of the set of
moving objects
Their proper recognition and detection followed by
visualization in specific applications
References
1. R. Boulic, P. Fua, L. Herda, M. Silaghi, J.S. Monzani, L. Nedel, and
D. Thalmann.An Anatomic Human Body for Motion Capture. In Technologies for
the Information Society: Developments and Opportunities. EMMSEC98, 1998.
2. J. Lasenby and A. Stevenson. Using Geometric Algebra for Optical Motion
Capture. In E.Bayro-Corrochano and G. Sobcyzk, editors, Applied Clifford
Algebras in Computer Science and Engineering. Birkhauser, Boston, U.S.A., 2000.
3. M. Kubíček. Using Dragonfly IEEE-1394 Digital Camera and Image Acquisition
Toolbox. In Sborník konference MATLAB 2004, pages 280–282. VŠCHT Praha,
2004.
4. M. Nixon and A. Aguado. Feature Extraction & Image Processing. NewNes
Elsevier, 2004.
5. M. Ringer, T. Drummond, and J. Lasenby. Using Occlusions to Aid Position
Estimation for Visual Motion Capture. In Proc Computer Vision and Pattern
Recoginition (CVPR). IEEE USA, 2001.
6. M. Ringer and J. Lasenby. Modelling and Tracking of Articulated Motion from
Multiple Camera Views. In Proc. British Machine Vision Conf (BMVC), pages
172–181, 2000.
Thank You!
http://dsp.vscht.cz

VIDEO PROCESSING IN MOTION MODELLING

Transkript

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