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


Abstract
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.
Anthology ID:
L12-1152
Volume:
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)
Month:
May
Year:
2012
Address:
Istanbul, Turkey
Venue:
LREC
SIG:
Publisher:
European Language Resources Association (ELRA)
Note:
Pages:
2421–2425
Language:
URL:
http://www.lrec-conf.org/proceedings/lrec2012/pdf/321_Paper.pdf
DOI:
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PDF:
http://www.lrec-conf.org/proceedings/lrec2012/pdf/321_Paper.pdf