Dalarna University's logo and link to the university's website

du.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • chicago-author-date
  • chicago-note-bibliography
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Real-Time Recognition System for Traffic Signs
Dalarna University, School of Technology and Business Studies, Computer Engineering.
2008 (English)Independent thesis Advanced level (degree of Master (Two Years))Student thesis
Abstract [en]

The aim of this thesis project is to develop the Traffic Sign Recognition algorithm for real time. In real time environment, vehicles move at high speed on roads. For the vehicle intelligent system it becomes essential to detect, process and recognize the traffic sign which is coming in front of vehicle with high relative velocity, at the right time, so that the driver would be able to pro-act simultaneously on instructions given in the Traffic Sign. The system assists drivers about traffic signs they did not recognize before passing them. With the Traffic Sign Recognition system, the vehicle becomes aware of the traffic environment and reacts according to the situation. The objective of the project is to develop a system which can recognize the traffic signs in real time. The three target parameters are the system’s response time in real-time video streaming, the traffic sign recognition speed in still images and the recognition accuracy. The system consists of three processes; the traffic sign detection, the traffic sign recognition and the traffic sign tracking. The detection process uses physical properties of traffic signs based on a priori knowledge to detect road signs. It generates the road sign image as the input to the recognition process. The recognition process is implemented using the Pattern Matching algorithm. The system was first tested on stationary images where it showed on average 97% accuracy with the average processing time of 0.15 seconds for traffic sign recognition. This procedure was then applied to the real time video streaming. Finally the tracking of traffic signs was developed using Blob tracking which showed the average recognition accuracy to 95% in real time and improved the system’s average response time to 0.04 seconds. This project has been implemented in C-language using the Open Computer Vision Library.

Place, publisher, year, edition, pages
Borlänge, 2008. , p. 87
Keywords [en]
recognition, traffic signs, realtime
Identifiers
URN: urn:nbn:se:du-3486OAI: oai:dalea.du.se:3486DiVA, id: diva2:518463
Uppsok
Technology
Supervisors
Available from: 2008-11-19 Created: 2008-11-19 Last updated: 2012-04-24Bibliographically approved

Open Access in DiVA

fulltext(1316 kB)4074 downloads
File information
File name FULLTEXT01.pdfFile size 1316 kBChecksum SHA-512
44925e79279225f83b4b93603a855318e9edac3726125fdef653107c3aad20a97c041d0823b2681a36306be3bbef2406892de8d1ed8d74b257235d82453ae56c
Type fulltextMimetype application/pdf

By organisation
Computer Engineering

Search outside of DiVA

GoogleGoogle Scholar
Total: 4092 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 1444 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • chicago-author-date
  • chicago-note-bibliography
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf