Rising popularity of motion capture in movie-production makes this technology more robust and more accessible. Utilization of this technology for sign language capturing and analysis is evident. The article deals with the usability of the motion capture in creating sign language corpora. A large amount of the data acquired by the motion capture has to be processed to provide usable data for wide range of research areas: e.g. sign language recognition, translation, synthesis, linguistics, etc. The aim of this article is to explore possible methods to detect interesting events in data using machine learning techniques. The result is a method for detection of the beginning and the end of the sign, hand location, finger and palm orientation, whether the sign is one or two handed, and symmetry in the two-handed signs.
@inproceedings{jedlicka:16022:sign-lang:lrec,
author = {Jedli{\v c}ka, Pavel and Kr{\v n}oul, Zden{\v e}k and {\v Z}elezn{\'y}, Milo{\v s}},
title = {Methods for Recognizing Interesting Events within Sign Language Motion Capture Data},
pages = {101--104},
editor = {Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Kristoffersen, Jette and Mesch, Johanna},
booktitle = {Proceedings of the {LREC2016} 7th Workshop on the Representation and Processing of Sign Languages: Corpus Mining},
maintitle = {10th International Conference on Language Resources and Evaluation ({LREC} 2016)},
publisher = {{European Language Resources Association (ELRA)}},
address = {Portoro{\v z}, Slovenia},
day = {28},
month = may,
year = {2016},
language = {english},
url = {https://www.sign-lang.uni-hamburg.de/lrec/pub/16022.pdf}
}