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Traffic Sign Recognition. Jacob Carlson Sean St. Onge Advisor: Dr. Thomas L. Stewart. Traffic Sign Recognition. Project Overview System Description Current Functionality Future Work. Traffic Sign Recognition. Project Overview System Description Current Functionality Future Work.
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Traffic Sign Recognition Jacob Carlson Sean St. Onge Advisor: Dr. Thomas L. Stewart
Traffic Sign Recognition • Project Overview • System Description • Current Functionality • Future Work
Traffic Sign Recognition • Project Overview • System Description • Current Functionality • Future Work
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.
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.
Traffic Sign Recognition • Project Overview • System Description • Current Functionality • Future Work
System Description • 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.
System Description • This portion of the program will gather and analyze color data, and will also perform edge detection. Red Green Blue
System Description • Additional methods (dilation, opening, closing, erosion) may also be applied at this time. • The sign will be classified based on color.
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.
Traffic Sign Recognition • Project Overview • System Description • Current Functionality • Future Work
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.
Current Functionality • Initial Image
Current Functionality • Red Plane
Current Functionality • Red Plane, after Thresholding
Current Functionality • Green Plane
Current Functionality • Blue Plane
Current Functionality • Threshold red plane after median filter.
Current Functionality • Sobel Masks – Used for edge detection (differentiation).
Current Functionality • Horizontal Edge Detection using Sobel masks.
Current Functionality • Vertical Edge Detection using Sobel masks.
Current Functionality • Sum of horizontal and vertical edge detection.
Current Functionality • Image after erosion by a line structuring element.
Current Functionality • Image after closing with octagon structuring element.
Current Functionality • Stop sign identified using ‘blob’ recognition techniques.
Current Functionality • Final image with stop sign highlighted.
Traffic Sign Recognition • Project Overview • System Description • Current Functionality • Future Work
Traffic Sign Recognition • Current problem is having the computer recognize that the shape is a stop sign. *
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.