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

Steps to achieve the goal Aims and motivation of the project 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. VideoOCX® software can handle all kinds of ‘Video for Windows’ ® compatible devices. Flyvideo 98 video capture card Microsoft Visual Basic 6.0 Phantom- a set of functions for the VB 6.0 that helps us control the LEGO™ vehicle.

Analyze the visual data and translate it into controlling commands for the vehicle Calculate the distance of the vehicle from traffic sign. Capture one frame from the video camera. Decide whether there is a traffic sign in the frame or not. If there is, recognize the traffic sign. NO X 25 cm GO RIGHT If vehicle is close enough to the sign send control command to RCX. YES

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 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.

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. Understanding HSV color space

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. Send control command according to recognized sign. If number of colored pixels suits a known sign, in a sufficient distance Example: Blue – 434 pixels Red – 591 pixels

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.