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 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 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 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 Region database Region joining Border extraction Input Sobel Conversion to grayscale Region extension Color detection Conversion to 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, Intel, “Open Computer Vision Library, Reference Manual”, 2001, D. A. Forsyth, J. Ponce, “Computer Vision: A Modern Approach”, Prentice Hall, 2003 George Stockman, Linda G. Shapiro, “Computer Vision”, Prentice Hall, 2001