TY - GEN
T1 - Computer-Vision Based Attention Monitoring for Online Meetings
AU - Dacayan, Tristram
AU - Kwak, Daehan
AU - Zhang, Xudong
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Due to the appearance of COVID-19, virtual video conferencing platforms like Zoom and Google Meet have become one of the main alternative ways to conduct virtual meetings and presentations. While the virtual platforms are cheaper and more flexible, presenters and meeting hosts are likely less efficient at assessing audience attention and engagement due to the lack of body language. In this paper, we propose a system for estimating and monitoring participant attention in virtual meetings by using computer vision. Our approach mainly focuses on changes in a person's presence, gaze direction, and head orientation as a computer camera has a limited field of view. We first created a module to detect and extract participant video cells to isolate users and process their attention individually. Using those videos, we then monitored the user's presence, using YOLOv3 and DeepSORT, and their gaze direction and head orientation, using PTGaze. Through this monitoring, the system is able to record and graph a user's attention over the total amount of frames and return a collective attention level graph for the entire meeting. We believe that our system has potential usage in settings where attention is critical, such as academic lectures or collaborative business meetings.
AB - Due to the appearance of COVID-19, virtual video conferencing platforms like Zoom and Google Meet have become one of the main alternative ways to conduct virtual meetings and presentations. While the virtual platforms are cheaper and more flexible, presenters and meeting hosts are likely less efficient at assessing audience attention and engagement due to the lack of body language. In this paper, we propose a system for estimating and monitoring participant attention in virtual meetings by using computer vision. Our approach mainly focuses on changes in a person's presence, gaze direction, and head orientation as a computer camera has a limited field of view. We first created a module to detect and extract participant video cells to isolate users and process their attention individually. Using those videos, we then monitored the user's presence, using YOLOv3 and DeepSORT, and their gaze direction and head orientation, using PTGaze. Through this monitoring, the system is able to record and graph a user's attention over the total amount of frames and return a collective attention level graph for the entire meeting. We believe that our system has potential usage in settings where attention is critical, such as academic lectures or collaborative business meetings.
KW - attention monitoring
KW - class monitoring
KW - computer vision
KW - gaze estimation
KW - head orientation
UR - http://www.scopus.com/inward/record.url?scp=85141161123&partnerID=8YFLogxK
U2 - 10.1109/PRAI55851.2022.9904097
DO - 10.1109/PRAI55851.2022.9904097
M3 - Conference contribution
AN - SCOPUS:85141161123
T3 - 2022 5th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2022
SP - 533
EP - 538
BT - 2022 5th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2022
Y2 - 19 August 2022 through 21 August 2022
ER -