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Robocup Vision Tracking with Xetal Processor. Edge and colour-based object detection. Sebastien Pierrot. Supervisors: Harry Broers (CFT), Anteneh Abbo, Richard Kleihorst (NATLAB). Outline. Introduction Vision System Object Tracking Future Work. Introduction (1). - PowerPoint PPT Presentation
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Robocup Vision Tracking with Xetal Processor
Edge and colour-based object detection
Sebastien Pierrot
Supervisors: Harry Broers (CFT),
Anteneh Abbo,
Richard Kleihorst (NATLAB)
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Outline
• Introduction
• Vision System
• Object Tracking
• Future Work
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Introduction (1)
Object Colour Ball Orange Field and Corners Green Goals Blue and
Yellow Walls and Lines White Robots and Logos on walls
Black
Team Shirts Magenta and Cyan
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Machine Vision
Blobs Analysis
Capture
ObjectDetection
Compression
Communication
Pixel to worldtranslation
Introduction (2)
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Outline
• Introduction• Vision System
– Robocup vision system– Xetal Architecture– Task division
• Object Tracking • Future Work
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Actual Robocup vision systemColour processing
colour camera FugaRGB-YUV box
High-Speed Monochrome Processing
B/W camera Fuga
Vision system (1)
Robocup vision system evolution
Xetal processor
Color MOS
Sensor
Trimedia Digital I/O
Xetal processor
B/W CMOS Sensor
Trimedia Digital I/O
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TRIMEDIA CHIPTRIMEDIA CHIP
XETALCHIPXETALCHIP
Camera
Repartition Tasks
Blobs Analysis
Capture
ObjectDetection
Compression
Communication
Pixels to worldtranslation
Communication
Communication
Vision system (3)
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Outline
• Introduction• System Vision• Object Tracking
– Color-based detection– Edge detection
• Future Work
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Color-based detection (1)
RGB
(0,0,0)
(0,1,0)
(1,0,0)
Green
Black
Blue(0,0,1)
Yellow
Cyan
White(1,1,1)
Magenta
3-D RGB cube
Object Tracking (1)
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YUV color space
Y : Luminosity
U,V: Chromatic components
Y=0.3*R+0.58*G+0.12*B
U=0.17*R-0.33*G+0.5*B
V=0.5*R-0.42*G+0.08*B
Color-based detection (2)Object Tracking (2)
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HSV color space
V = ( R + G + B )/3
S = ( 1 - min(R,G,B)/ V )
H = 0 + (G-B)/ if max is R
= 1/3 + (B-R)/ if max is G
= 2/3 + (R-G)/ if max is B
is the (max-min) of the RGBs
Color-based detection (3)Object Tracking (3)
V : Value
S :Saturation
H : Hue
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HSI color space
=/2 if G>B=3/2 if G<BH=1 if G=B
Blue
I
Cyan
Red
GreenYellow
Magenta
Black
S
H
3
2
2
320.5
3arctanα2π1
GBRI
BGBGRS
BGIRH
Color-based detection (4)Object Tracking (4)
I : Intensity
S :Saturation
H : Hue
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Segmentation examples
U
V
S
H
Linear
Constant
Color-based detection (5)Object Tracking (5)
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Object Tracking (6)Color-based detection (6)
Orange YUV segmentation
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Object Tracking (7)Color-based detection (7)
Orange HSI segmentation
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Implementation discussion• HSV
4 variable divisions• HSI
One variable divisionArc tangent function
Conclusion: • YUV linear segmentation for quicker processing• HSI constant segmentation for tuning facility and better
color density
Color-based detection (8)Object Tracking (8)
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Goal• Strong intensity contrast detection
• Divide the image into areas
corresponding to different objects
• Reducing image informations
ComputationEstimated with the maximum of the 1st derivative or with
the zero crossing of the 2nd derivative
Edge detection (1)Object Tracking (9)
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Sobel edge detectorApproximation absolute gradient magnitude at each point in an input grayscale image
a pair of 3×3 convolution kernels
• Advantage: Simple implementation
• Drawback: Sensible to the noise
Edge detection (2)Object Tracking (10)
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Canny edge detectorMore sophisticated: multi-stage process
• Advantages– Simple thresholing
– Lower sensibility to the noise
– Large patterns: 5*5,7*7…
• Drawbacks– Larger code program
Edge detection (3)Object Tracking (11)
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G’’(2)G’(0)
PE index -2 -1 0 1 2 3
Line Mem0 a11 a12 a13 a14 a15 a16 a17
Line Mem1 a21 a22 a23 a24 a25 a26 a27
Line Mem2 …..
Line Mem3 …..
Line Mem4 …..
Line Mem5 a61 a62 a63 a64 a65 a66 a67
Line Mem6 a71 a72 a73 a74 a75 a76 a77
7*7 Kernel
Example: 7*7 pattern elaboration
• Shifts
• Sum of intermediate
Results:
Gx/y(0)= G’(0)+G’’(2)
Gx/y(1)= G’(1)+G’’(3)
Edge detection (4)Object Tracking (12)
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Canny detector
Sobel detector
Edge detection (5)Object Tracking (13)
Results
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Outline
• Introduction• System Vision• Object Tracking • Future Work
– Edge detection tuning– Data compression