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How to find balls in images of a moving humanoid robot using neural networks.Talk from RoboCup Workshop 07 in Pittsburgh.
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A B I N J O: U C L C R I
Hannes Schulz, Hauke Strasdat, and Sven Behnke
University of FreiburgInstitute of Computer Science
Nov 29, 2007
Balls and Non-Balls Finding Ball Candidates Classification of Ball Candidates Experiments
M
The ball
is small,
is easy to confuse with other objects
is the most important object on the field:You cannot play sensibly without knowing its position
Balls and Non-Balls Finding Ball Candidates Classification of Ball Candidates Experiments
M
The ball
is small,
is easy to confuse with other objects
is the most important object on the field:You cannot play sensibly without knowing its position
Balls and Non-Balls Finding Ball Candidates Classification of Ball Candidates Experiments
M
The ball
is small,
is easy to confuse with other objects
is the most important object on the field:You cannot play sensibly without knowing its position
Balls and Non-Balls Finding Ball Candidates Classification of Ball Candidates Experiments
M
The ball
is small,
is easy to confuse with other objects
is the most important object on the field:You cannot play sensibly without knowing its position
. We should put a lot of effort into finding the single real ball.
O
1 I B N-B
2 F B C
3 C B C
4 E
O
1 I B N-B
2 F B C
3 C B C
4 E
Balls and Non-Balls Finding Ball Candidates Classification of Ball Candidates Experiments
H D B L L?
Balls and Non-Balls Finding Ball Candidates Classification of Ball Candidates Experiments
H D B L L?
T E C
Balls and Non-Balls Finding Ball Candidates Classification of Ball Candidates Experiments
H D B L L?
L C
Balls and Non-Balls Finding Ball Candidates Classification of Ball Candidates Experiments
H D B L L?
M B
Balls and Non-Balls Finding Ball Candidates Classification of Ball Candidates Experiments
H D B L L?
CW L
Balls and Non-Balls Finding Ball Candidates Classification of Ball Candidates Experiments
H D B L L?
C R
Balls and Non-Balls Finding Ball Candidates Classification of Ball Candidates Experiments
H D N-B L L?
Balls and Non-Balls Finding Ball Candidates Classification of Ball Candidates Experiments
H D N-B L L?
H F
Balls and Non-Balls Finding Ball Candidates Classification of Ball Candidates Experiments
H D N-B L L?
O O
Balls and Non-Balls Finding Ball Candidates Classification of Ball Candidates Experiments
H D N-B L L?
O O
Balls and Non-Balls Finding Ball Candidates Classification of Ball Candidates Experiments
H D N-B L L?
F
Balls and Non-Balls Finding Ball Candidates Classification of Ball Candidates Experiments
H D N-B L L?
F
O
1 I B N-B
2 F B C
3 C B C
4 E
Balls and Non-Balls Finding Ball Candidates Classification of Ball Candidates Experiments
C E YUV S
Actual ball color
Wider, brownish color
. Allows for motion blur
Balls and Non-Balls Finding Ball Candidates Classification of Ball Candidates Experiments
C E YUV S
Two ellipses for “orange”
Actual ball color
Wider, brownish color
. Allows for motion blur
Balls and Non-Balls Finding Ball Candidates Classification of Ball Candidates Experiments
C E YUV S
Two ellipses for “orange”
Actual ball color
Wider, brownish color
. Allows for motion blur
Balls and Non-Balls Finding Ball Candidates Classification of Ball Candidates Experiments
S C I
orange
white
green
(-candidate)
64 : 1
YUV Camera Image
Balls and Non-Balls Finding Ball Candidates Classification of Ball Candidates Experiments
S C I
orange
white
green
(-candidate)
64 : 1
1. Find Maximum
2. Find Weighted Mean
Balls and Non-Balls Finding Ball Candidates Classification of Ball Candidates Experiments
S C I
orange
white
green
(-candidate)
64 : 1
cut corresponding area
Balls and Non-Balls Finding Ball Candidates Classification of Ball Candidates Experiments
S C I
orange
white
green
(-candidate)
64 : 1
cut corresponding area
Box size depends on position in image
O
1 I B N-B
2 F B C
3 C B C
4 E
Balls and Non-Balls Finding Ball Candidates Classification of Ball Candidates Experiments
P C
. Projection changes with ellipses: Robust to changes inlighting conditions
Balls and Non-Balls Finding Ball Candidates Classification of Ball Candidates Experiments
P C
. Projection changes with ellipses: Robust to changes inlighting conditions
Balls and Non-Balls Finding Ball Candidates Classification of Ball Candidates Experiments
P C
. Projection changes with ellipses: Robust to changes inlighting conditions
Balls and Non-Balls Finding Ball Candidates Classification of Ball Candidates Experiments
P L
YUV-Image Y-Image Subsampled Y-Image
. Subtraction of mean: Robust to changes in lighting conditions
Balls and Non-Balls Finding Ball Candidates Classification of Ball Candidates Experiments
H L F
Balls and Non-Balls Finding Ball Candidates Classification of Ball Candidates Experiments
H L F
Balls and Non-Balls Finding Ball Candidates Classification of Ball Candidates Experiments
H L F
+
-
- -
-
+
-
- -
-
+
-
- -
-
+
-
- -
-
Balls and Non-Balls Finding Ball Candidates Classification of Ball Candidates Experiments
N N C
+
-
- -
-
1: Ball
0: No Ball
O
1 I B N-B
2 F B C
3 C B C
4 E
Balls and Non-Balls Finding Ball Candidates Classification of Ball Candidates Experiments
NR KS 06 – T
H520 MHz ARM PocketPC
VGA HTC Camera
D S160 balls
440 non-balls
divided randomly in training set(80%) and test set (20%)
P (T S)100% of distractors classifiedcorrectly
1 ball out of 32 not recognized
Balls and Non-Balls Finding Ball Candidates Classification of Ball Candidates Experiments
NR KS 06 – T
H520 MHz ARM PocketPC
VGA HTC Camera
D S160 balls
440 non-balls
divided randomly in training set(80%) and test set (20%)
P (T S)100% of distractors classifiedcorrectly
1 ball out of 32 not recognized
Balls and Non-Balls Finding Ball Candidates Classification of Ball Candidates Experiments
NR KS 06 – R T
H520 MHz ARM PocketPC
VGA HTC Camera
R T T
Robot decides autonomouslywhich object to approach.
Balls and Non-Balls Finding Ball Candidates Classification of Ball Candidates Experiments
NR KS 2007
H1.33 GHz PC
WVGA µEye Camera
Balls and Non-Balls Finding Ball Candidates Classification of Ball Candidates Experiments
NR KS 2007
S D D S273 balls
548 non-balls
training set 62%, validation set13%, test set 25%
varying lighting conditions
Balls and Non-Balls Finding Ball Candidates Classification of Ball Candidates Experiments
NR KS 2007
A SAvg Luminance
ball non-ball
Avg Orange-Greennessball non-ball
D S273 balls
548 non-balls
training set 62%, validation set13%, test set 25%
varying lighting conditions
Balls and Non-Balls Finding Ball Candidates Classification of Ball Candidates Experiments
NR KS 2007
A SAvg Luminance
ball non-ball
Avg Orange-Greennessball non-ball
D S273 balls
548 non-balls
training set 62%, validation set13%, test set 25%
varying lighting conditions
R T S91.1% accuracy
76.6% if stimuli flipped up/down.Drop suggests dependency ongradient.
88.2% if lighting in testset differs.Classifier seems indifferent.
Balls and Non-Balls Finding Ball Candidates Classification of Ball Candidates Experiments
C C
Classifier
Task Neural Net Linear Classifier KNN (k = 5)
Regular 91.1% 86.5% 88.5%Flipped 74.0% 70.0% 76.6%
Balls and Non-Balls Finding Ball Candidates Classification of Ball Candidates Experiments
C
The ball has properties aside from being orange
These properties are exploited by our Neural NetworkClassifiers
Changes in Lighting conditions can be dealt with by projectionto lines in YUV-space and Luminance.
The method introduced here can be generalized to othersmall-sized objects on the field.