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Road Sign Recognition based on Invariant Features using Support Vector Machine
Dalarna University, School of Technology and Business Studies, Computer Engineering.
2007 (English)Independent thesis Advanced level (degree of Master (Two Years))Student thesis
Abstract [en]

Since last two decades researches have been working on developing systems that can assists drivers in the best way possible and make driving safe. Computer vision has played a crucial part in design of these systems. With the introduction of vision techniques various autonomous and robust real-time traffic automation systems have been designed such as Traffic monitoring, Traffic related parameter estimation and intelligent vehicles. Among these automatic detection and recognition of road signs has became an interesting research topic. The system can assist drivers about signs they don’t recognize before passing them. Aim of this research project is to present an Intelligent Road Sign Recognition System based on state-of-the-art technique, the Support Vector Machine. The project is an extension to the work done at ITS research Platform at Dalarna University [25]. Focus of this research work is on the recognition of road signs under analysis. When classifying an image its location, size and orientation in the image plane are its irrelevant features and one way to get rid of this ambiguity is to extract those features which are invariant under the above mentioned transformation. These invariant features are then used in Support Vector Machine for classification. Support Vector Machine is a supervised learning machine that solves problem in higher dimension with the help of Kernel functions and is best know for classification problems.

Place, publisher, year, edition, pages
Borlänge, 2007. , 88 p.
Keyword [en]
Speed-limit Recognition, Shape Recognition, Support Vector Machines, Kernel Functions, Invariant Features, Feature space.
Identifiers
URN: urn:nbn:se:du-2760OAI: oai:dalea.du.se:2760DiVA: diva2:518213
Uppsok
Technology
Supervisors
Available from: 2007-04-18 Created: 2007-04-18 Last updated: 2012-04-24Bibliographically approved

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fulltext(4054 kB)2005 downloads
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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