M.S. (Master of Science)
Department of Electrical Engineering
Autonomous vehicle development is currently progressing at a very fast pace and traffic sign detection and classification has an important role in it. This thesis looks at the history of autonomous vehicles as well as different implementations for traffic sign detection and classification. Multiple possible approaches are analyzed with the final goal of doing this task in real-time using a portable system.
To accomplish this task, the final solution uses a convolutional neural network for detection and classification combined with a custom optical character recognition algorithm for speed limit signs. The optical character recognition algorithm is built from the ground up using a combination of custom code and basic OpenCV color manipulation. The training and testing dataset is based on a combination of the Belgian Dataset, German Dataset, as well as images taken while driving in Illinois, United States. The final results are compared against other research papers in this field.
Ciuntu, Victor, "Real-Time Traffic Sign Detection and Classification Based on a Video Feed" (2020). Graduate Research Theses & Dissertations. 6927.
Northern Illinois University
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