@inproceedings{2eacd298ae75435cb549514cf0e52f37,
title = "Multi-domain Vision based Sign Language Recognition based on Auto Labeled Hand Tracking Data Learning",
abstract = "Remote operating and autonomous systems are widely applied in various fields, and the development of technology for human machine interface and communication is strongly demanded. In order to overcome the limitations of the conventional keyboard and tablet devices, various vision sensors and state-of-the-art artificial intelligence image processing techniques are used to recognize hand gestures. In this study, we propose a method for recognizing a reference sign language using auto labeled AI model training datasets. This study can be applied to the remote control interfaces for drivers to vehicles, person to home appliances, and gamers to entertainment contents and remote character input technology for the metaverse environment.",
keywords = "deep learning, hand tracking, human machine interface, machine learning, multi-domain sensing, object detection, remote sensing, sign language recognition",
author = "Junha Lee and Won, {Hong In} and Kim, {Min Young} and Kim, {Byeong Hak}",
note = "Publisher Copyright: {\textcopyright} 2022 SPIE.; Image and Signal Processing for Remote Sensing XXVIII 2022 ; Conference date: 05-09-2022 Through 06-09-2022",
year = "2022",
doi = "10.1117/12.2638450",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Lorenzo Bruzzone and Francesca Bovolo and Nazzareno Pierdicca",
booktitle = "Image and Signal Processing for Remote Sensing XXVIII",
address = "United States",
}