VIDEO PROCESSINGIN 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
VIDEO PROCESSING IN MOTION MODELLING – Miroslav KUBÍČEK, Aleš PROCHÁZKA, Aleš PAVELKA – Process Control 2005 – p.2/10
Contents
Introduction
System Description
VIDEO PROCESSING IN MOTION MODELLING – Miroslav KUBÍČEK, Aleš PROCHÁZKA, Aleš PAVELKA – Process Control 2005 – p.2/10
Contents
Introduction
System Description
Three-Dimensional Object Detection
VIDEO PROCESSING IN MOTION MODELLING – Miroslav KUBÍČEK, Aleš PROCHÁZKA, Aleš PAVELKA – Process Control 2005 – p.2/10
Contents
Introduction
System Description
Three-Dimensional Object Detection
Motion Visualization
VIDEO PROCESSING IN MOTION MODELLING – Miroslav KUBÍČEK, Aleš PROCHÁZKA, Aleš PAVELKA – Process Control 2005 – p.2/10
Contents
Introduction
System Description
Three-Dimensional Object Detection
Motion Visualization
Conclusions
VIDEO PROCESSING IN MOTION MODELLING – Miroslav KUBÍČEK, Aleš PROCHÁZKA, Aleš PAVELKA – Process Control 2005 – p.2/10
Introduction
Goals of the projectStudy of image acquisition using synchronized twocamera system and A/D convertorsStudy of mathematical methods for bodylocalization in the three dimensional spaceVisualization of the body movement using virtualreality environment
VIDEO PROCESSING IN MOTION MODELLING – Miroslav KUBÍČEK, Aleš PROCHÁZKA, Aleš PAVELKA – Process Control 2005 – p.3/10
Introduction
Goals of the projectStudy of image acquisition using synchronized twocamera system and A/D convertorsStudy of mathematical methods for bodylocalization in the three dimensional spaceVisualization of the body movement using virtualreality environment
ApplicationModelling of the body movementAnalysis of the object movementusing several reference points
VIDEO PROCESSING IN MOTION MODELLING – Miroslav KUBÍČEK, Aleš PROCHÁZKA, Aleš PAVELKA – Process Control 2005 – p.3/10
System Description
Technical details
Cameras with color CCDsensor and with resolution1024x768, 30 fps
Synchronization 125 µs
Connection with computervia IEEE 1394
Direct connection to theMATLAB system and ImageAcquisition Toolbox
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Light
ACamera
BCamera
IEEE 1394
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&MATLAB
Image Acquisition Tlbx
Computer
VIDEO PROCESSING IN MOTION MODELLING – Miroslav KUBÍČEK, Aleš PROCHÁZKA, Aleš PAVELKA – Process Control 2005 – p.4/10
Three-Dimensional Object Detection
Principle of the object localisation
FRONT VIEW
C [xC
(1),0]
TOP VIEW
A [xA,y
A]
Camera A
B [xB,y
B]
Camera B
C [xC
(1),yC
(1)]
α1(1) β
1(1)c
b1(1) a
1(1)
(a) INITIAL POSITIONING
FRONT VIEW
C [xC
(k),zC
(k)]
α2(k) β
2(k)
b2(k) a
2(k)
TOP VIEW
A [xA,y
A]
Camera A
B [xB,y
B]
Camera B
C [xC
(k),yC
(k)]
α1(k) β
1(k)c
b1(k) a
1(k)
(b) K−TH POSITION
VIDEO PROCESSING IN MOTION MODELLING – Miroslav KUBÍČEK, Aleš PROCHÁZKA, Aleš PAVELKA – Process Control 2005 – p.5/10
Three-Dimensional Object Detection
Principle of the object localisation
FRONT VIEW
C [xC
(1),0]
TOP VIEW
A [xA,y
A]
Camera A
B [xB,y
B]
Camera B
C [xC
(1),yC
(1)]
α1(1) β
1(1)c
b1(1) a
1(1)
(a) INITIAL POSITIONING
FRONT VIEW
C [xC
(k),zC
(k)]
α2(k) β
2(k)
b2(k) a
2(k)
TOP VIEW
A [xA,y
A]
Camera A
B [xB,y
B]
Camera B
C [xC
(k),yC
(k)]
α1(k) β
1(k)c
b1(k) a
1(k)
(b) K−TH POSITION
α1(1) = arccos((b1(1)
2 + c2 − a1(1)2)/(2 b1(1) c))
β1(1) = arccos((a1(1)
2 + c2 − b1(1)2)/(2 a1(1) c))
VIDEO PROCESSING IN MOTION MODELLING – Miroslav KUBÍČEK, Aleš PROCHÁZKA, Aleš PAVELKA – Process Control 2005 – p.5/10
Three-Dimensional Object Detection
Calibration of the camera systemHORIZONTAL AND VERTICAL CAMERA ANGLE EVALUATION
(a) CALIBRATION GRID
CAMERA svertical/2
shorizontal
/2
d
d
(b) INITIAL LIGHT POSITIONING
α1min
α1(1)
α1max
α2min
α2(1)
α2max
VIDEO PROCESSING IN MOTION MODELLING – Miroslav KUBÍČEK, Aleš PROCHÁZKA, Aleš PAVELKA – Process Control 2005 – p.6/10
Three-Dimensional Object Detection
Calibration of the camera systemHORIZONTAL AND VERTICAL CAMERA ANGLE EVALUATION
(a) CALIBRATION GRID
CAMERA svertical/2
shorizontal
/2
d
d
(b) INITIAL LIGHT POSITIONING
α1min
α1(1)
α1max
α2min
α2(1)
α2max
αhorizontal = 2 arctan(shorizontal/2/d
)
αvertical = 2 arctan(svertical/2/d
)
VIDEO PROCESSING IN MOTION MODELLING – Miroslav KUBÍČEK, Aleš PROCHÁZKA, Aleš PAVELKA – Process Control 2005 – p.6/10
Motion Visualization
VIDEO PROCESSING IN MOTION MODELLING – Miroslav KUBÍČEK, Aleš PROCHÁZKA, Aleš PAVELKA – Process Control 2005 – p.7/10
Motion Visualization
VIDEO PROCESSING IN MOTION MODELLING – Miroslav KUBÍČEK, Aleš PROCHÁZKA, Aleš PAVELKA – Process Control 2005 – p.7/10
Motion Visualization
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−5
0
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CAMERA B
x−axis
MOTION MODELLING
CAMERA A
910
8
11
29
7
30
28
12
6
27
26
513
254
14
y−axis
24
153
23
2
16
22117
21
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z−ax
is
VIDEO PROCESSING IN MOTION MODELLING – Miroslav KUBÍČEK, Aleš PROCHÁZKA, Aleš PAVELKA – Process Control 2005 – p.7/10
Motion Visualization
0
500
1000
1500
0
500
1000
1500
−5
0
5
10
15
20
25
30
CAMERA B
x−axis
MOTION MODELLING
CAMERA A
910
8
11
29
7
30
28
12
6
27
26
513
254
14
y−axis
24
153
23
2
16
22117
21
18
19
20
z−ax
is
Bally.translation
VR Sink
Scope
simin
FromWorkspace
0
Display
VIDEO PROCESSING IN MOTION MODELLING – Miroslav KUBÍČEK, Aleš PROCHÁZKA, Aleš PAVELKA – Process Control 2005 – p.7/10
Motion Visualization
0
500
1000
1500
0
500
1000
1500
−5
0
5
10
15
20
25
30
CAMERA B
x−axis
MOTION MODELLING
CAMERA A
910
8
11
29
7
30
28
12
6
27
26
513
254
14
y−axis
24
153
23
2
16
22117
21
18
19
20
z−ax
is
Bally.translation
VR Sink
Scope
simin
FromWorkspace
0
Display
VIDEO PROCESSING IN MOTION MODELLING – Miroslav KUBÍČEK, Aleš PROCHÁZKA, Aleš PAVELKA – Process Control 2005 – p.7/10
Conclusions
Results
VIDEO PROCESSING IN MOTION MODELLING – Miroslav KUBÍČEK, Aleš PROCHÁZKA, Aleš PAVELKA – Process Control 2005 – p.8/10
Conclusions
ResultsSuccessfully tested system with one moving object
VIDEO PROCESSING IN MOTION MODELLING – Miroslav KUBÍČEK, Aleš PROCHÁZKA, Aleš PAVELKA – Process Control 2005 – p.8/10
Conclusions
ResultsSuccessfully tested system with one moving objectCreation of the virtual reality model based on thereal movement
VIDEO PROCESSING IN MOTION MODELLING – Miroslav KUBÍČEK, Aleš PROCHÁZKA, Aleš PAVELKA – Process Control 2005 – p.8/10
Conclusions
ResultsSuccessfully tested system with one moving objectCreation of the virtual reality model based on thereal movement
Further Research
VIDEO PROCESSING IN MOTION MODELLING – Miroslav KUBÍČEK, Aleš PROCHÁZKA, Aleš PAVELKA – Process Control 2005 – p.8/10
Conclusions
ResultsSuccessfully tested system with one moving objectCreation of the virtual reality model based on thereal movement
Further ResearchDeterministic and statistical analysis of the set ofmoving objects
VIDEO PROCESSING IN MOTION MODELLING – Miroslav KUBÍČEK, Aleš PROCHÁZKA, Aleš PAVELKA – Process Control 2005 – p.8/10
Conclusions
ResultsSuccessfully tested system with one moving objectCreation of the virtual reality model based on thereal movement
Further ResearchDeterministic and statistical analysis of the set ofmoving objectsTheir proper recognition and detection followed byvisualization in specific applications
VIDEO PROCESSING IN MOTION MODELLING – Miroslav KUBÍČEK, Aleš PROCHÁZKA, Aleš PAVELKA – Process Control 2005 – p.8/10
References
1. R. Boulic, P. Fua, L. Herda, M. Silaghi, J.S. Monzani, L. Nedel, andD. Thalmann.An Anatomic Human Body for Motion Capture. In Technologies forthe Information Society: Developments and Opportunities. EMMSEC98, 1998.
2. J. Lasenby and A. Stevenson. Using Geometric Algebra for Optical MotionCapture. In E.Bayro-Corrochano and G. Sobcyzk, editors, Applied CliffordAlgebras in Computer Science and Engineering. Birkhauser, Boston, U.S.A., 2000.
3. M. Kubíček. Using Dragonfly IEEE-1394 Digital Camera and Image AcquisitionToolbox. In Sborník konference MATLAB 2004, pages 280–282. VŠCHT Praha,2004.
4. M. Nixon and A. Aguado. Feature Extraction & Image Processing. NewNesElsevier, 2004.
5. M. Ringer, T. Drummond, and J. Lasenby. Using Occlusions to Aid PositionEstimation for Visual Motion Capture. In Proc Computer Vision and PatternRecoginition (CVPR). IEEE USA, 2001.
6. M. Ringer and J. Lasenby. Modelling and Tracking of Articulated Motion fromMultiple Camera Views. In Proc. British Machine Vision Conf (BMVC), pages172–181, 2000.
VIDEO PROCESSING IN MOTION MODELLING – Miroslav KUBÍČEK, Aleš PROCHÁZKA, Aleš PAVELKA – Process Control 2005 – p.9/10
Thank You!http://dsp.vscht.cz
VIDEO PROCESSING IN MOTION MODELLING – Miroslav KUBÍČEK, Aleš PROCHÁZKA, Aleš PAVELKA – Process Control 2005 – p.10/10
ContentsIntroductionSystem DescriptionThree-Dimensional Object DetectionThree-Dimensional Object DetectionMotion VisualizationConclusionsReferences
