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

Présentation au sujet: "Traffic Sign Recognition Jacob Carlson Sean St. Onge Advisor: Dr. Thomas L. Stewart."— Transcription de la présentation:

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

2 Traffic Sign Recognition  Project Overview  System Description  Performance Expectations  Preliminary Work  Schedule of Tasks

3 Traffic Sign Recognition  Project Overview  System Description  Performance Expectations  Preliminary Work  Schedule of Tasks

4 Traffic Sign Recognition 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 Traffic Sign Recognition  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.  The software should be able to function in spite of these issues.

6 Traffic Sign Recognition  Project Overview  System Description  Performance Expectations  Preliminary Work  Schedule of Tasks

7 Traffic Sign Recognition

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 algorithms will be applied, if necessary.

9 Traffic Sign Recognition  Once the image is adjusted as needed, the software will apply the main image processing algorithms.  This portion of the program will gather and analyze color data, and will also perform edge detection.  Additional methods requiring further research may also be applied at this time.

10 Traffic Sign Recognition  If a sign is identified in the image, it will be classified.  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.

11 Traffic Sign Recognition  Project Overview  System Description  Performance Expectations  Preliminary Work  Schedule of Tasks

12 Traffic Sign Recognition  Sample calculation for real-time implementation:  Image size of 500x500 pixels.  250,000 data points time three color planes. This gives 750,000 data points.  At 30 frames per second this equates to 22.5 million data points per second.

13 Traffic Sign Recognition  At 22.5 million data points per second the speed required for processing images is too ambitious for this project.  For this reason we will process discrete images only.

14 Traffic Sign Recognition  Project Overview  System Description  Performance Expectations  Preliminary Work  Schedule of Tasks

15 Traffic Sign Recognition  Design team has done preliminary research into the fundamentals of image processing.  Design team has completed several digital image processing tutorials and experimented with basic color detection.

16 Traffic Sign Recognition  Add color plane breakdown here.

17 Traffic Sign Recognition  Average intensity.

18 Traffic Sign Recognition  Project Overview  System Description  Performance Expectations  Preliminary Work  Schedule of Tasks

19 Traffic Sign Recognition

20  If sufficient progress is made on this problem, the design team may also decide to apply some of these image processing algorithms to camouflage recognition.

21 Questions?