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On the Agreement of Commodity 3D Cameras
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM). (DISA;DISA-IDP;DISTA)ORCID iD: 0000-0002-8591-1035
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM). (DISA;DISA-IDP;DISTA)ORCID iD: 0000-0001-9062-1609
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM). (DISA;DISA-IDP;DISTA)ORCID iD: 0000-0002-7565-3714
AIMO AB.
2019 (English)In: 23rd International Conference on Image Processing, Computer Vision, & Pattern Recognition (IPCV'19: July 29 - August 1, 2019, USA), CSREA Press, 2019Conference paper, Published paper (Refereed)
Abstract [en]

The advent of commodity 3D sensor technol- ogy has, amongst other things, enabled the efficient and effective assessment of human movements. Machine learning approaches do not rely manual definitions of gold standards for each new movement. However, to train models for the automated assessments of a new movement they still need a lot of data that map recorded movements to expert judg- ments. As camera technology changes, this training needs to be repeated if a new camera does not agree with the old one. The present paper presents an inexpensive method to check the agreement of cameras, which, in turn, would allow for a safe reuse of trained models regardless of the cameras. We apply the method to the Kinect, Astra Mini, and Real Sense cameras. The results show that these cameras do not agree and that the models cannot be reused without an unacceptable decay in accuracy. However, the suggested method works independent of movements and cameras and could potentially save effort when integrating new cameras in an existing assessment environment.

Place, publisher, year, edition, pages
CSREA Press, 2019.
Keywords [en]
3D camera agreement, human movement assessment
National Category
Computer Sciences
Research subject
Computer and Information Sciences Computer Science
Identifiers
URN: urn:nbn:se:lnu:diva-89180OAI: oai:DiVA.org:lnu-89180DiVA, id: diva2:1352261
Conference
3rd International Conference on Image Processing, Computer Vision, & Pattern Recognition
Available from: 2019-09-18 Created: 2019-09-18 Last updated: 2019-09-18

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Hagelbäck, JohanLincke, AlisaLöwe, Welf

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CiteExportLink to record
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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