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Traffic Sign Identification

Traffic Sign Identification. Team G Project 15. Team members. Lajos Rodek - Szeged, Hungary Marcin Rogucki - Lodz, Poland Mircea Nanu   - Timisoara, Romania     Selman Kulac - Ankara, Turkey     Zsolt Husz - Timisoara, Romania. Lajos Rodek. Sign recognition ideas

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Traffic Sign Identification

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  1. Traffic SignIdentification Team GProject 15

  2. Team members • Lajos Rodek - Szeged, Hungary • Marcin Rogucki - Lodz, Poland • Mircea Nanu   - Timisoara, Romania •     Selman Kulac - Ankara, Turkey •     Zsolt Husz - Timisoara, Romania

  3. Lajos Rodek • Sign recognition ideas • Sign library preparation • Presentation • Lots of laughing

  4. Marcin Rogucki • Sign recognition coding • Sign recognition ideas • Sign detection ideas • Presentation

  5. Mircea Nanu • Sign detection ideas • Sign detection coding • Web page preparation • Moral support and jokes

  6. Selman Kulac • Gathering sign images • General ideas • Presentation

  7. Zsolt Husz • Sign detection coding • Sign detection ideas • Picture acquisition • Many, many testing

  8. Our goal • Final goal: to detect and identify all traffic sign in arbitrary images

  9. Assumptions • No human interaction • No preprocessing of the image • Flexible handling of images • Image is not rotated by more than 30 degrees • Images can contain any number of signs or no signs at all • Only daylight images are taken • At most ¼ of a sign may be covered • No background constrains / limitations

  10. General program idea Program consists of two separated problems: • Detecting signs on the image • Recognizing detected regions of possible sign locations

  11. Sign detection 1 Signs features: • Well defined colors with high saturation • They are rather homogenous • Sharp contours • Known basic shapes • Allowed colors: • Red, blue (dominant colors) • Yellow • Green (very rare) • White, black (found mostly inside of signs)

  12. Sign detection 2 Main steps: • Edge detection (3 by 3 Sobel) • Converting image to HSV color space • Reducing number of colors • Segmentation relying on the color • Marking probable signs with boundary boxes • Joining adjacent regions • Removing background

  13. Conversion to grayscale Sobel Input Region extension Conversion toHSV Color detection Border extraction Region joining Region database Output Sign detection 3

  14. Sign recognition 1 Input: • Picture containing at most one sign (subrange of the original image) with eliminated background • Sign templates and names Output: • Sign name in case it is a traffic sign • Localization on the image

  15. Sign recognition 2 Tasks: • Detecting the shape of a sign • Finding corners if necessary • Transforming the shape (Perspective/rotation  Facing/upright) • Color unification • Comparison with templates

  16. Sign recognition 3 Detecting the shape: • Building a chain code • Computing angles between vectors • Checking number of the corners • Defining a shape (triangle,square,circle)

  17. Sign recognition 4 Finding corners: • “Charged particles” based approach Particles run away from each other and locate corners as furthest possible points in the figure

  18. Sign recognition 5 Transforming the sign: • Inverse texture mapping according to the corners and shape

  19. Sign recognition 6 Color unification: • Simplifying colors depending on similarity • Allowed colors: Red, green, blue, yellow, white, black, background (pink) • Computing a histogram

  20. Sign recognition 7 Comparison with a template: • Normalized histograms are compared resulting in a RMS measure • Raster pictures are compared pixel by pixel • Probability based decision

  21. Results 1

  22. Results 2

  23. Results 3

  24. Achievements • Everything works fine • Every team member is happy • Signs are detected and recognized correctly in most cases • All assumptions are met • Works even in unusual cases (e.g. night pictures)

  25. Future improvements • Better reliability with fast motion blurring • More independency with illumination • Robustness on sign detection (fine-tuning the heuristically adopted constrains) • Better library templates • Speed-ups • Adaptation for a sequence of images

  26. Thank you for your attention!

  27. References • Intel, “Intel Image Processing Library, Reference Manual”, 2000, http://developer.intel.com • Intel, “Open Computer Vision Library, Reference Manual”,2001, http://developer.intel.com • D. A. Forsyth, J. Ponce, “Computer Vision: A Modern Approach”, Prentice Hall, 2003 • George Stockman, Linda G. Shapiro, “Computer Vision”, Prentice Hall, 2001

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