1. Traffic Sign Recognition Jacob Carlson Sean St. Onge Advisor: Dr. Thomas L. Stewart

2. Traffic Sign Recognition  Project Overview  System Description  Current Functionality  Future Work

3. Traffic Sign Recognition  Project Overview  System Description  Current Functionality  Future Work

4. Project Overview Object identification has many applications in various fields. This project aims to identify a traffic sign from a digital image. This would be useful in an autonomous vehicle application. These ideas and methods could also be used in other areas.

5. Project Overview  The overall objective of this project is to write a program what will identify a traffic sign from a digital photograph.  Traffic signs appear in diverse background situations and, at times, may be partially obscured.

6. Traffic Sign Recognition  Project Overview  System Description  Current Functionality  Future Work

7. System Description

8.  When the program is initialized, an image, previously saved on the system’s hard drive, is loaded for analysis.  At this point, some preliminary analysis will be performed, and preprocessing will be performed manually.

9. System Description  This portion of the program will gather and analyze color data, and will also perform edge detection. Red Green Blue

10. System Description  Additional methods (dilation, opening, closing, erosion) may also be applied at this time.  The sign will be classified based on color.

11. System Description  After classification, the software will highlight the image or “area of interest”.  The software will then write pertinent data to either the screen or an output file.

12. Traffic Sign Recognition  Project Overview  System Description  Current Functionality  Future Work

13. Current Functionality  Currently our program divides the color image into the three color planes.  We first look for red signs (stop sign, do not enter, wrong way). Our algorithm currently isolates most red signs effectively.  It can also isolate yellow signs, but this still requires some optimization.

14. Current Functionality  Initial Image

15. Current Functionality  Red Plane

16. Current Functionality  Red Plane, after Thresholding

17. Current Functionality  Green Plane

18. Current Functionality  Blue Plane

19. Current Functionality  Threshold red plane after median filter.

20. Current Functionality  Sobel Masks – Used for edge detection (differentiation).

21. Current Functionality  Horizontal Edge Detection using Sobel masks.

22. Current Functionality  Vertical Edge Detection using Sobel masks.

23. Current Functionality  Sum of horizontal and vertical edge detection.

24. Current Functionality  Image after erosion by a line structuring element.

25. Current Functionality  Image after closing with octagon structuring element.

26. Current Functionality  Stop sign identified using ‘blob’ recognition techniques.

27. Current Functionality  Final image with stop sign highlighted.

28. Traffic Sign Recognition  Project Overview  System Description  Current Functionality  Future Work

29. Traffic Sign Recognition  Current problem is having the computer recognize that the shape is a stop sign. *

30. Traffic Sign Recognition  Identifying a region of interest and cropping out the background prior to performing main processing would streamline calculations.  Speed could also be increased by using C or C++ to implement the processing algorithms.

31. Questions?