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A B I N J O: U C 
 L  C R  I

  Hannes Schulz, Hauke Strasdat, an...
Balls and Non-Balls        Finding Ball Candidates   Classification of Ball Candidates   Experiments


 M




    ...
Balls and Non-Balls        Finding Ball Candidates   Classification of Ball Candidates   Experiments


 M




    ...
Balls and Non-Balls        Finding Ball Candidates   Classification of Ball Candidates   Experiments


 M




    ...
Balls and Non-Balls        Finding Ball Candidates   Classification of Ball Candidates   Experiments


 M




    ...
O



  1   I  B  N-B


  2   F B C


  3   C  B C

...
O



  1   I  B  N-B


  2   F B C


  3   C  B C

...
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?
...
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...
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...
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...
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...
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...
O



  1   I  B  N-B


  2   F B C


  3   C  B C

...
Balls and Non-Balls   Finding Ball Candidates   Classification of Ball Candidates   Experiments


 C  E  YU...
Balls and Non-Balls   Finding Ball Candidates     Classification of Ball Candidates   Experiments


 C  E  ...
Balls and Non-Balls   Finding Ball Candidates     Classification of Ball Candidates   Experiments


 C  E  ...
Balls and Non-Balls   Finding Ball Candidates            Classification of Ball Candidates     Experiments


 S ...
Balls and Non-Balls   Finding Ball Candidates            Classification of Ball Candidates     Experiments


 S ...
Balls and Non-Balls   Finding Ball Candidates            Classification of Ball Candidates     Experiments


 S ...
Balls and Non-Balls     Finding Ball Candidates            Classification of Ball Candidates     Experiments


 S...
O



  1   I  B  N-B


  2   F B C


  3   C  B C

...
Balls and Non-Balls   Finding Ball Candidates   Classification of Ball Candidates   Experiments


 P  C
Balls and Non-Balls      Finding Ball Candidates   Classification of Ball Candidates   Experiments


 P  C

...
Balls and Non-Balls      Finding Ball Candidates   Classification of Ball Candidates   Experiments


 P  C

...
Balls and Non-Balls      Finding Ball Candidates         Classification of Ball Candidates   Experiments


 P  L...
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


...
O



  1   I  B  N-B


  2   F B C


  3   C  B C

...
Balls and Non-Balls   Finding Ball Candidates              Classification of Ball Candidates   Experiments


 NR KS...
Balls and Non-Balls   Finding Ball Candidates              Classification of Ball Candidates   Experiments


 NR KS...
Balls and Non-Balls   Finding Ball Candidates           Classification of Ball Candidates   Experiments


 NR KS 0...
Balls and Non-Balls   Finding Ball Candidates    Classification of Ball Candidates   Experiments


 NR KS 2007

  ...
Balls and Non-Balls   Finding Ball Candidates              Classification of Ball Candidates   Experiments


 NR KS...
Balls and Non-Balls   Finding Ball Candidates              Classification of Ball Candidates   Experiments


 NR KS...
Balls and Non-Balls   Finding Ball Candidates              Classification of Ball Candidates   Experiments


 NR KS...
Balls and Non-Balls       Finding Ball Candidates          Classification of Ball Candidates   Experiments


 C ...
Balls and Non-Balls      Finding Ball Candidates   Classification of Ball Candidates   Experiments


 C




      ...
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The ball is not just orange.

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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.

Published in: Technology, Sports, Business
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Transcript of "The ball is not just orange."

  1. 1. A B I N J O: U C  L  C R  I Hannes Schulz, Hauke Strasdat, and Sven Behnke University of Freiburg Institute of Computer Science Nov 29, 2007
  2. 2. Balls and Non-Balls Finding Ball Candidates Classification of Ball Candidates Experiments M The ball is small,
  3. 3. Balls and Non-Balls Finding Ball Candidates Classification of Ball Candidates Experiments M The ball is small, is easy to confuse with other objects
  4. 4. 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
  5. 5. 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.
  6. 6. O 1 I  B  N-B 2 F B C 3 C  B C 4 E
  7. 7. O 1 I  B  N-B 2 F B C 3 C  B C 4 E
  8. 8. Balls and Non-Balls Finding Ball Candidates Classification of Ball Candidates Experiments H D B L L?
  9. 9. Balls and Non-Balls Finding Ball Candidates Classification of Ball Candidates Experiments H D B L L? T E C
  10. 10. Balls and Non-Balls Finding Ball Candidates Classification of Ball Candidates Experiments H D B L L? L C
  11. 11. Balls and Non-Balls Finding Ball Candidates Classification of Ball Candidates Experiments H D B L L? M B
  12. 12. Balls and Non-Balls Finding Ball Candidates Classification of Ball Candidates Experiments H D B L L? C W L
  13. 13. Balls and Non-Balls Finding Ball Candidates Classification of Ball Candidates Experiments H D B L L? C R
  14. 14. Balls and Non-Balls Finding Ball Candidates Classification of Ball Candidates Experiments H D N-B L L?
  15. 15. Balls and Non-Balls Finding Ball Candidates Classification of Ball Candidates Experiments H D N-B L L? H  F
  16. 16. Balls and Non-Balls Finding Ball Candidates Classification of Ball Candidates Experiments H D N-B L L? O O
  17. 17. Balls and Non-Balls Finding Ball Candidates Classification of Ball Candidates Experiments H D N-B L L? O O
  18. 18. Balls and Non-Balls Finding Ball Candidates Classification of Ball Candidates Experiments H D N-B L L? F
  19. 19. Balls and Non-Balls Finding Ball Candidates Classification of Ball Candidates Experiments H D N-B L L? F
  20. 20. O 1 I  B  N-B 2 F B C 3 C  B C 4 E
  21. 21. Balls and Non-Balls Finding Ball Candidates Classification of Ball Candidates Experiments C  E  YUV S
  22. 22. 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
  23. 23. 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
  24. 24. Balls and Non-Balls Finding Ball Candidates Classification of Ball Candidates Experiments S  C I orange YUV Camera Image (-candidate) white green 64 : 1
  25. 25. Balls and Non-Balls Finding Ball Candidates Classification of Ball Candidates Experiments S  C I 1. Find Maximum 2. Find Weighted Mean orange (-candidate) white green 64 : 1
  26. 26. Balls and Non-Balls Finding Ball Candidates Classification of Ball Candidates Experiments S  C I orange cut corresponding area (-candidate) white green 64 : 1
  27. 27. Balls and Non-Balls Finding Ball Candidates Classification of Ball Candidates Experiments S  C I orange cut corresponding area (-candidate) white Box size depends on position in image green 64 : 1
  28. 28. O 1 I  B  N-B 2 F B C 3 C  B C 4 E
  29. 29. Balls and Non-Balls Finding Ball Candidates Classification of Ball Candidates Experiments P  C
  30. 30. Balls and Non-Balls Finding Ball Candidates Classification of Ball Candidates Experiments P  C Projection changes with ellipses: Robust to changes in lighting conditions
  31. 31. Balls and Non-Balls Finding Ball Candidates Classification of Ball Candidates Experiments P  C Projection changes with ellipses: Robust to changes in lighting conditions
  32. 32. 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
  33. 33. Balls and Non-Balls Finding Ball Candidates Classification of Ball Candidates Experiments H L F
  34. 34. Balls and Non-Balls Finding Ball Candidates Classification of Ball Candidates Experiments H L F
  35. 35. Balls and Non-Balls Finding Ball Candidates Classification of Ball Candidates Experiments H L F - - + - - - - + - -
  36. 36. Balls and Non-Balls Finding Ball Candidates Classification of Ball Candidates Experiments N N C 1: Ball 0: No Ball - - + - -
  37. 37. O 1 I  B  N-B 2 F B C 3 C  B C 4 E
  38. 38. Balls and Non-Balls Finding Ball Candidates Classification of Ball Candidates Experiments NR KS 06 – T H 520 MHz ARM PocketPC VGA HTC Camera D S 160 balls 440 non-balls divided randomly in training set (80%) and test set (20%)
  39. 39. Balls and Non-Balls Finding Ball Candidates Classification of Ball Candidates Experiments NR KS 06 – T H 520 MHz ARM PocketPC VGA HTC Camera D S 160 balls 440 non-balls divided randomly in training set (80%) and test set (20%) P (T S) 100% of distractors classified correctly 1 ball out of 32 not recognized
  40. 40. Balls and Non-Balls Finding Ball Candidates Classification of Ball Candidates Experiments NR KS 06 – R T H 520 MHz ARM PocketPC VGA HTC Camera R T T Robot decides autonomously which object to approach.
  41. 41. Balls and Non-Balls Finding Ball Candidates Classification of Ball Candidates Experiments NR KS 2007 H 1.33 GHz PC WVGA µEye Camera
  42. 42. Balls and Non-Balls Finding Ball Candidates Classification of Ball Candidates Experiments NR KS 2007 S D D S 273 balls 548 non-balls training set 62%, validation set 13%, test set 25% varying lighting conditions
  43. 43. Balls and Non-Balls Finding Ball Candidates Classification of Ball Candidates Experiments NR KS 2007 A S D S Avg Luminance 273 balls ball non-ball 548 non-balls training set 62%, validation set 13%, test set 25% varying lighting conditions Avg Orange-Greenness ball non-ball
  44. 44. Balls and Non-Balls Finding Ball Candidates Classification of Ball Candidates Experiments NR KS 2007 A S D S Avg Luminance 273 balls ball non-ball 548 non-balls training set 62%, validation set 13%, test set 25% varying lighting conditions Avg Orange-Greenness R  T S ball non-ball 91.1% accuracy 76.6% if stimuli flipped up/down. Drop suggests dependency on gradient. 88.2% if lighting in testset differs. Classifier seems indifferent.
  45. 45. 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%
  46. 46. 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 Network Classifiers Changes in Lighting conditions can be dealt with by projection to lines in YUV-space and Luminance. The method introduced here can be generalized to other small-sized objects on the field.
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