Traffic Sign Detection and Recognition
Keywords:Classification and Regression Tree (CART), Quality Assurance (QA), Predictive Model Markup Language (PMML), Convolutional Neural Network (CNN), Support vector
The objective of this work is the development of an algorithm for the automatic recognition of traffic signs. Two major problems exist in the process of detection and recognition of traffic signals. Road signs are frequently occluded partially by other vehicles and many objects are present in traffic scenes which make the sign detection hard and pedestrians, other vehicles, buildings and billboards may confuse the detection system by patterns similar to that of road signs. Also, color information from traffic scene images is affected by varying illumination caused by weather conditions, time (day night) and shadowing. This method detects the location of the sign in the image, based on its geometrical characteristics and recognizes it using color information. Partial occlusion is dealt by the use of the Hough Transform.
How to Cite
Copyright (c) 2021 Harshil Patel, Yash Kundariya, Fenil Patel, Harshal Shah
This work is licensed under a Creative Commons Attribution 4.0 International License.