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

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • 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