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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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Presentation Transcript
team members
Team members
  • Lajos Rodek - Szeged, Hungary
  • Marcin Rogucki - Lodz, Poland
  • Mircea Nanu   - Timisoara, Romania
  •     Selman Kulac - Ankara, Turkey
  •     Zsolt Husz - Timisoara, Romania
lajos rodek
Lajos Rodek
  • Sign recognition ideas
  • Sign library preparation
  • Presentation
  • Lots of laughing
marcin rogucki
Marcin Rogucki
  • Sign recognition coding
  • Sign recognition ideas
  • Sign detection ideas
  • Presentation
mircea nanu
Mircea Nanu
  • Sign detection ideas
  • Sign detection coding
  • Web page preparation
  • Moral support and jokes
selman kulac
Selman Kulac
  • Gathering sign images
  • General ideas
  • Presentation
zsolt husz
Zsolt Husz
  • Sign detection coding
  • Sign detection ideas
  • Picture acquisition
  • Many, many testing
our goal
Our goal
  • Final goal: to detect and identify all traffic sign in arbitrary images
assumptions
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
general program idea
General program idea

Program consists of two separated problems:

  • Detecting signs on the image
  • Recognizing detected regions of possible sign locations
sign detection 1
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)
sign detection 2
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
sign detection 3
Conversion

to grayscale

Sobel

Input

Region

extension

Conversion

toHSV

Color

detection

Border

extraction

Region

joining

Region

database

Output

Sign detection 3
sign recognition 1
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
sign recognition 2
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
sign recognition 3
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)

sign recognition 4
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

sign recognition 5
Sign recognition 5

Transforming the sign:

  • Inverse texture mapping according to the corners and shape
sign recognition 6
Sign recognition 6

Color unification:

  • Simplifying colors depending on similarity
      • Allowed colors:

Red, green, blue, yellow, white, black, background (pink)

  • Computing a histogram
sign recognition 7
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
achievements
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)
future improvements
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
references
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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