Comparing computer vision analysis of signed language video with motion capture recordings

Matti Karppa, Tommi Jantunen, Ville Viitaniemi, Jorma Laaksonen, Birgitta Burger, Danny De Weerdt

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

    3 Citations (Scopus)


    We consider a non-intrusive computer-vision method for measuring the motion of a person performing natural signing in video recordings. The quality and usefulness of the method is compared to a traditional marker-based motion capture set-up. The accuracy of descriptors extracted from video footage is assessed qualitatively in the context of sign language analysis by examining if the shape of the curves produced by the different means resemble one another in sequences where the shape could be a source of valuable linguistic information. Then, quantitative comparison is performed first by correlating the computer-vision-based descriptors with the variables gathered with the motion capture equipment. Finally, multivariate linear and non-linar regression methods are applied for predicting the motion capture variables based on combinations of computer vision descriptors. The results show that even the simple computer vision method evaluated in this paper can produce promisingly good results for assisting researchers working on sign language analysis.
    Original languageEnglish
    Title of host publication8th Language Resources and Evaluation Conference (LREC 2012), Istanbul, Turkey, May 21-27,2012
    Place of PublicationIstanbul
    Publication statusPublished - 2012
    MoE publication typeA4 Article in a conference publication
    EventInternational Conference on Language Resources and Evaluation - Istanbul, Turkey
    Duration: 21 May 201227 May 2012
    Conference number: 8


    ConferenceInternational Conference on Language Resources and Evaluation
    Abbreviated titleLREC


    • Sign language
    • Motion capture
    • Computer vision
    • Multivariate regression analysis


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