Presented by:
Doron Brot, Maimon Vanunu, Elia Tzirulnick
Supervised by:
Johanan Erez, Ina Krinsky, Dror Ouzana
Vision & Image Science Laboratory, Department of Electrical Engineering,Technion
Aims and motivation of the project
Steps to achieve the goal
Algorithm for Traffics Signs Recognition
Results
Conclusions
Control a self navigating vehicle
according to traffic signs
• Build a controllable vehicle.
• Attach a wireless camera to the vehicle.
• Capture pictures from camera to computer.
• Analyze the visual data and translate it into controlling
commands for the vehicle
Build a controllable vehicle.
Mindstorms Robot
Invention System
Build a controllable vehicle
Build a controllable vehicle.
Communication to PC
through Infra-red transmitter.
Attach a wireless camera to the vehicle.
WAT-207CD CCD Color Camera
Wireless video transmitter
Attach a wireless camera to the vehicle.
Wireless connection
between camera and PC
Capture pictures from camera to computer.
Flyvideo 98 video capture card
Phantom- a set of functions
for the VB 6.0 that helps us
control the LEGO™ vehicle.
VideoOCX® software can
handle all kinds of ‘Video
for Windows’ ®
compatible devices.
Microsoft Visual Basic 6.0
Analyze the visual data and translate it into controlling commands for
the vehicle
Capture one frame from the video camera.
Decide whether there is a traffic sign in the
frame or not.
YES
NO
If there is, recognize the traffic sign.
X
Calculate the distance of the vehicle from
traffic sign.
If vehicle is close enough to the sign
25 cm
send control command to RCX.
GO
RIGHT
• How humans see colors.
• Conversion from RGB to HSV color space.
• Use Saturation in order to find colored areas in frame.
• Analyze the colored areas according to Hue.
• Recognize traffic sign.
• Sum colored pixels to calculate distance to the traffic sign.
• Send control command according to recognized sign.
The human eye
Visible Light
‫‪The Retina‬‬
‫שני סוגי קולטנים‪:‬‬
‫• קנים (‪)Rods‬‬
‫• מדוכים (‪)Cones‬‬
‫הקולטנים ברשתית‬
http://www-cvrl.ucsd.edu/ :‫מקור‬
Image representation in computer file – graylevel image.
Image representation in computer file – color image.
RGB values of traffic sign images
Not very helpful !
Conversion from RGB to HSV color space.
The HSV color space (hue,
saturation, value) is often used by
people because it corresponds better
to how people experience color than
the RGB color space does.
Understanding HSV color space
As hue varies, the corresponding colors vary
from red, through yellow, green, cyan, blue,
and magenta, back to red.
Understanding HSV color space
As saturation varies, the corresponding colors
(hues) vary from unsaturated (shades of gray)
to fully saturated (no white component).
Understanding HSV color space
As value, or brightness, varies, the corresponding
colors become increasingly brighter.
Use Saturation in order to find colored areas in each frame.
Analyze the colored areas according to Hue.
Recognize traffic sign.
For example:
If the hue value of any pixel is
between 200 and 250 that means
that the color is red so we
painted the pixel pure red.
Sum colored pixels to calculate distance to the traffic sign.
Example:
Blue – 434 pixels
Red – 591 pixels
If number of colored pixels suits a known sign, in a sufficient distance
Send control command according to recognized sign.
Graphical User Interface - GUI
The navigating vehicle.
• Successful recognition of traffic signs of different colors.
• White – gray background was helpful.
• For real traffic sign recognition more sophisticated
algorithms have to be used (colored background, real-time
processing etc).
• The vehicle can only recognize the traffic signs we
programmed it to )“Turn Right”, “No Parking” and “Stop”(.
We would like to thank our mentors: Johanan Erez, Ina Krinsky and
Dror Ouzana.
Thanks to our counselors: Adva, Eran , May-Tal and koby.
Thanks to Ort Management.
We would also like to thank the Ollendorff Research Center for its
support.
http://visl.technion.ac.il/projects/scitech02