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Robocup Vision Tracking with Xetal Processor

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).

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Robocup Vision Tracking with Xetal Processor

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  1. Robocup Vision Tracking with Xetal Processor Edge and colour-based object detection Sebastien Pierrot Supervisors: Harry Broers (CFT), Anteneh Abbo, Richard Kleihorst (NATLAB)

  2. Outline • Introduction • Vision System • Object Tracking • Future Work

  3. Introduction (1) Robocup

  4. Capture Object Detection Compression Blobs Analysis Pixel to world translation Communication Introduction (2) Machine Vision

  5. Outline • Introduction • Vision System • Robocup vision system • Xetal Architecture • Task division • Object Tracking • Future Work

  6. Colour processing RGB-YUV box Fuga colour camera High-Speed Monochrome Processing Fuga B/W camera Robocup vision system evolution B/W CMOS Sensor Color MOS Sensor Digital I/O Digital I/O Xetal processor Xetal processor Trimedia Trimedia Vision system (1) Actual Robocup vision system

  7. Vision system (2) Xetal Architecture Block Schema

  8. TRIMEDIA CHIP Camera XETALCHIP Capture Communication Object Detection Blobs Analysis Compression Pixels to world translation Communication Communication Vision system (3) Repartition Tasks

  9. Outline • Introduction • System Vision • Object Tracking • Color-based detection • Edge detection • Future Work

  10. Object Tracking (1) Color-based detection (1) RGB 3-D RGB cube

  11. Object Tracking (2) Color-based detection (2) 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

  12. Object Tracking (3) Color-based detection (3) HSV color space V : Value S :Saturation H : Hue 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

  13. Green Yellow Cyan S I H Black Red Blue Magenta Object Tracking (4) Color-based detection (4) HSI color space I : Intensity S :Saturation H : Hue =/2 if G>B =3/2 if G<B H=1 if G=B

  14. Object Tracking (5) Color-based detection (5) V Segmentation examples U Linear S Constant H

  15. Object Tracking (6) Color-based detection (6) Orange YUV segmentation

  16. Object Tracking (7) Color-based detection (7) Orange HSI segmentation

  17. Object Tracking (8) Color-based detection (8) Implementation discussion • HSV 4 variable divisions • HSI One variable division Arc tangent function Conclusion: • YUV linear segmentation for quicker processing • HSI constant segmentation for tuning facility and better color density

  18. Object Tracking (9) Edge detection (1) Goal • Strong intensity contrast detection • Divide the image into areas corresponding to different objects • Reducing image informations Computation Estimated with the maximum of the 1st derivative or with the zero crossing of the 2nd derivative

  19. Object Tracking (10) Edge detection (2) Sobel edge detector Approximation 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

  20. Object Tracking (11) Edge detection (3) Canny edge detector More sophisticated: multi-stage process • Advantages • Simple thresholing • Lower sensibility to the noise • Large patterns: 5*5,7*7… • Drawbacks • Larger code program

  21. Object Tracking (12) Edge detection (4) • Example: 7*7 pattern elaboration • Shifts • Sum of intermediate • Results: • Gx/y(0)= G’(0)+G’’(2) • Gx/y(1)= G’(1)+G’’(3) 7*7 Kernel G’’(2) G’(0)

  22. Object Tracking (13) Edge detection (5) Sobel detector Results Canny detector

  23. Outline • Introduction • System Vision • Object Tracking • Future Work • Edge detection tuning • Data compression

  24. Compression • Goal: Reducing space information • Proposed format: • Features • Processor identification (PID) • Statistic information delivering from serial processor • Shifts for blank elimination

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