Traffic SignIdentification

Team GProject 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

Sign library preparation

Presentation

Lots of laughing

Marcin Rogucki

Sign recognition coding

Sign recognition ideas

Sign detection ideas

Presentation

Mircea Nanu

Sign detection ideas

Sign detection coding

Web page preparation

Moral support and jokes

Selman Kulac

Gathering sign images

General ideas

Presentation

Zsolt Husz

Sign detection coding

Sign detection ideas

Picture acquisition

Many, many testing

Our goal

Final goal: to detect and identify all traffic sign in arbitrary images

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

Program consists of two separated problems:

Detecting signs on the image

Recognizing detected regions of possible sign locations

Sign detection 1Signs 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

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

Regiondatabase

Regionjoining

Borderextraction

Input SobelConversionto grayscale

Regionextension

Colordetection

Conversionto HSV

Output

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

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

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

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

Transforming the sign: Inverse texture mapping according to the corners and shape

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

Comparison with a template: Normalized histograms are compared resulting in a RMS measure

Raster pictures are compared pixel by pixel

Probability based decision

Results 1

Results 2

Results 3

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

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

Thank you for your attention!

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