Road sign recognition for assisting drivers
For a highly reliable autonomous vehicle, it must be able to detect traffic signs and obey the traffic rules. In this paper, color segmentation is applied in YCbCr color space and shape detection is processed through blob analysis method. By calculating the circularity and area ratio with bounding b...
Saved in:
Main Author: | |
---|---|
Other Authors: | |
Format: | Final Year Project |
Language: | English |
Published: |
2016
|
Subjects: | |
Online Access: | http://hdl.handle.net/10356/67956 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
id |
sg-ntu-dr.10356-67956 |
---|---|
record_format |
dspace |
spelling |
sg-ntu-dr.10356-679562023-07-07T15:42:49Z Road sign recognition for assisting drivers Poon, Wai Chung Ma Kai Kuang School of Electrical and Electronic Engineering DRNTU::Engineering For a highly reliable autonomous vehicle, it must be able to detect traffic signs and obey the traffic rules. In this paper, color segmentation is applied in YCbCr color space and shape detection is processed through blob analysis method. By calculating the circularity and area ratio with bounding box, the shapes in binary images can be classified accurately. In the recognition stage, SURF algorithm is performed on both the training sets and sample images to extract the features points of the traffic signs for matching based on K-NN classifier. Bachelor of Engineering 2016-05-23T08:24:17Z 2016-05-23T08:24:17Z 2016 Final Year Project (FYP) http://hdl.handle.net/10356/67956 en Nanyang Technological University 43 p. application/pdf |
institution |
Nanyang Technological University |
building |
NTU Library |
continent |
Asia |
country |
Singapore Singapore |
content_provider |
NTU Library |
collection |
DR-NTU |
language |
English |
topic |
DRNTU::Engineering |
spellingShingle |
DRNTU::Engineering Poon, Wai Chung Road sign recognition for assisting drivers |
description |
For a highly reliable autonomous vehicle, it must be able to detect traffic signs and obey the traffic rules. In this paper, color segmentation is applied in YCbCr color space and shape detection is processed through blob analysis method. By calculating the circularity and area ratio with bounding box, the shapes in binary images can be classified accurately. In the recognition stage, SURF algorithm is performed on both the training sets and sample images to extract the features points of the traffic signs for matching based on K-NN classifier. |
author2 |
Ma Kai Kuang |
author_facet |
Ma Kai Kuang Poon, Wai Chung |
format |
Final Year Project |
author |
Poon, Wai Chung |
author_sort |
Poon, Wai Chung |
title |
Road sign recognition for assisting drivers |
title_short |
Road sign recognition for assisting drivers |
title_full |
Road sign recognition for assisting drivers |
title_fullStr |
Road sign recognition for assisting drivers |
title_full_unstemmed |
Road sign recognition for assisting drivers |
title_sort |
road sign recognition for assisting drivers |
publishDate |
2016 |
url |
http://hdl.handle.net/10356/67956 |
_version_ |
1772828699573354496 |