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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
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