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Occupant Detection using Computer Vision
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
2000 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

The purpose of this master’s thesis was to study the possibility to use computer vision methods to detect and classify objects in the front passenger seat in a car. This work presents different approaches to solve this problem and evaluates the usefulness of each technique. The classification information should later be used to modulate the speed and the force of the airbag, to be able to provide each occupant with optimal protection and safety.

This work shows that computer vision has a great potential in order to provide data, which may be used to perform reliable occupant classification. Future choice of method to use depends on many factors, for example costs and requirements on the system from laws and car manufacturers. Further, evaluation and tests of the methods in this thesis, other methods, the ABE approach and post-processing of the results should also be made before a reliable classification algorithm may be written.

Place, publisher, year, edition, pages
2000. , 69 p.
Series
LiTH-ISY-Ex, 3026
Keyword [en]
Occupant detection, passenger classification, adaptive airbags, stereo vision, motion, colour vision, face detection
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-54363ISRN: n/aOAI: oai:DiVA.org:liu-54363DiVA: diva2:303034
Presentation
(English)
Uppsok
Technology
Available from: 2010-03-10 Created: 2010-03-10 Last updated: 2010-03-30Bibliographically approved

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CiteExportLink to record
Permanent link

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