@article{ART002843517},
author={Geonwoo Ji and Changwon Lee and Jaeseok Yun},
title={Counting and Localizing Occupants using IR-UWB Radar and Machine Learning},
journal={Journal of The Korea Society of Computer and Information},
issn={1598-849X},
year={2022},
volume={27},
number={5},
pages={1-9},
doi={10.9708/jksci.2022.27.05.001}
TY - JOUR
AU - Geonwoo Ji
AU - Changwon Lee
AU - Jaeseok Yun
TI - Counting and Localizing Occupants using IR-UWB Radar and Machine Learning
JO - Journal of The Korea Society of Computer and Information
PY - 2022
VL - 27
IS - 5
PB - The Korean Society Of Computer And Information
SP - 1
EP - 9
SN - 1598-849X
AB - Localization systems can be used with various circumstances like measuring population movement and rescue technology, even in security technology (like infiltration detection system). Vision sensors such as camera often used for localization is susceptible with light and temperature, and can cause invasion of privacy. In this paper, we used ultra-wideband radar technology (which is not limited by aforementioned problems) and machine learning techniques to measure the number and location of occupants in other indoor spaces behind the wall. We used four different algorithms and compared their results, including extremely randomized tree for four different situations; detect the number of occupants in a classroom, split the classroom into 28 locations and check the position of occupant, select one out of the 28 locations, divide it into 16 fine-grained locations, and check the position of occupant, and checking the positions of two occupants (existing in different locations). Overall, four algorithms showed good results and we verified that detecting the number and location of occupants are possible with high accuracy using machine learning. Also we have considered the possibility of service expansion using the oneM2M standard platform and expect to develop more service and products if this technology is used in various fields.
KW - UWB radar;counting occupants;localization;machine learning;oneM2M
DO - 10.9708/jksci.2022.27.05.001
ER -
Geonwoo Ji, Changwon Lee and Jaeseok Yun. (2022). Counting and Localizing Occupants using IR-UWB Radar and Machine Learning. Journal of The Korea Society of Computer and Information, 27(5), 1-9.
Geonwoo Ji, Changwon Lee and Jaeseok Yun. 2022, "Counting and Localizing Occupants using IR-UWB Radar and Machine Learning", Journal of The Korea Society of Computer and Information, vol.27, no.5 pp.1-9. Available from: doi:10.9708/jksci.2022.27.05.001
Geonwoo Ji, Changwon Lee, Jaeseok Yun "Counting and Localizing Occupants using IR-UWB Radar and Machine Learning" Journal of The Korea Society of Computer and Information 27.5 pp.1-9 (2022) : 1.
Geonwoo Ji, Changwon Lee, Jaeseok Yun. Counting and Localizing Occupants using IR-UWB Radar and Machine Learning. 2022; 27(5), 1-9. Available from: doi:10.9708/jksci.2022.27.05.001
Geonwoo Ji, Changwon Lee and Jaeseok Yun. "Counting and Localizing Occupants using IR-UWB Radar and Machine Learning" Journal of The Korea Society of Computer and Information 27, no.5 (2022) : 1-9.doi: 10.9708/jksci.2022.27.05.001
Geonwoo Ji; Changwon Lee; Jaeseok Yun. Counting and Localizing Occupants using IR-UWB Radar and Machine Learning. Journal of The Korea Society of Computer and Information, 27(5), 1-9. doi: 10.9708/jksci.2022.27.05.001
Geonwoo Ji; Changwon Lee; Jaeseok Yun. Counting and Localizing Occupants using IR-UWB Radar and Machine Learning. Journal of The Korea Society of Computer and Information. 2022; 27(5) 1-9. doi: 10.9708/jksci.2022.27.05.001
Geonwoo Ji, Changwon Lee, Jaeseok Yun. Counting and Localizing Occupants using IR-UWB Radar and Machine Learning. 2022; 27(5), 1-9. Available from: doi:10.9708/jksci.2022.27.05.001
Geonwoo Ji, Changwon Lee and Jaeseok Yun. "Counting and Localizing Occupants using IR-UWB Radar and Machine Learning" Journal of The Korea Society of Computer and Information 27, no.5 (2022) : 1-9.doi: 10.9708/jksci.2022.27.05.001
