TY - GEN
T1 - Individual status recognition system assisted by UAV in post-disaster
AU - Ehara, Kakeru
AU - Aljehani, Maher
AU - Yokemura, Taketoshi
AU - Inoue, Masahiro
N1 - Funding Information:
This work was supported by JSPS KAKENHI Grant Number JP15K00929 and JP19K0315.
Publisher Copyright:
© 2020 IEEE.
PY - 2020/1
Y1 - 2020/1
N2 - When natural disasters occur, there is a possibility of having many injured people in the disaster area. In the meanwhile, rescue teams have to aid these injured individuals as fast as possible. In this study, we proposed a recognition system of individual status to help rescue teams. Employing Unmanned Aerial Vehicles (UAVs) system after disaster occurrence gives many advantages. For instance, a UAV can cover a wide area and provide aerial photographs in a short period. This study aims to classify whether an individual status is standing, sitting, or lying on the ground by using supervised machine learning. Experiments revealed that the system is able to recognize all three types of individual status with an accuracy of 95.6%. Moreover, the authors confirmed the usefulness of using a UAV to recognize individuals in the post-disaster scenario.
AB - When natural disasters occur, there is a possibility of having many injured people in the disaster area. In the meanwhile, rescue teams have to aid these injured individuals as fast as possible. In this study, we proposed a recognition system of individual status to help rescue teams. Employing Unmanned Aerial Vehicles (UAVs) system after disaster occurrence gives many advantages. For instance, a UAV can cover a wide area and provide aerial photographs in a short period. This study aims to classify whether an individual status is standing, sitting, or lying on the ground by using supervised machine learning. Experiments revealed that the system is able to recognize all three types of individual status with an accuracy of 95.6%. Moreover, the authors confirmed the usefulness of using a UAV to recognize individuals in the post-disaster scenario.
KW - Deep learning
KW - Disaster response
KW - Image processing
KW - Rescue missions
KW - Status recognition
KW - Unmanned Aerial Vehicles
UR - http://www.scopus.com/inward/record.url?scp=85082597006&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85082597006&partnerID=8YFLogxK
U2 - 10.1109/ICCE46568.2020.9043101
DO - 10.1109/ICCE46568.2020.9043101
M3 - Conference contribution
AN - SCOPUS:85082597006
T3 - Digest of Technical Papers - IEEE International Conference on Consumer Electronics
BT - 2020 IEEE International Conference on Consumer Electronics, ICCE 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE International Conference on Consumer Electronics, ICCE 2020
Y2 - 4 January 2020 through 6 January 2020
ER -