dc.contributor.advisor | Biswas, Rubel | |
dc.contributor.advisor | Mostakim, Moin | |
dc.contributor.author | Pinku, Subroto Nag | |
dc.date.accessioned | 2016-05-22T11:01:36Z | |
dc.date.available | 2016-05-22T11:01:36Z | |
dc.date.copyright | 2016 | |
dc.date.issued | 4/20/2016 | |
dc.identifier.other | ID 11201019 | |
dc.identifier.uri | http://hdl.handle.net/10361/5304 | |
dc.description | Cataloged from PDF version of thesis report. | |
dc.description | Includes bibliographical references (page 50-51). | |
dc.description | This thesis report is submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science and Engineering, 2016. | en_US |
dc.description.abstract | Traffic signs play a vital role in transportation system. Many road accidents occur due to
over speed. Detecting and classifying different group of Speed limit traffic signs can save our
lives as well as resources. In this research we propose a novel approach towards the detection of
these signs. In our proposed system with the help of color and non-color information of traffic
signs we first detect the presence of a sign and the classify it. For detection we used circle Hough
transformation along with segmentation and labeling. After extracting sign from a scene we
match the sign against a classified dataset. We used deformable spatial pyramid matching for
recognition of the sign. Once we find a match, our system returns the class or speed limit. Our
experiment shows that the recognition rate is very high and we compare our result with another
approach at the end. | en_US |
dc.description.statementofresponsibility | Subroto Nag Pinku | |
dc.format.extent | 51 pages | |
dc.language.iso | en | en_US |
dc.publisher | BRAC University | en_US |
dc.rights | BRAC University thesis are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. | |
dc.subject | Computer science and engineering | en_US |
dc.subject | CSE | en_US |
dc.subject | Traffic sign recognition | en_US |
dc.title | Speed limit traffic sign recognition in night mode based on deformable spatial pyramid | en_US |
dc.type | Thesis | en_US |
dc.contributor.department | Department of Computer Science and Engineering, BRAC University | |
dc.description.degree | B. Computer Science and Engineering | |