TY - GEN
T1 - Eating and drinking recognition via integrated information of head directions and joint positions in a group
AU - Ienaga, Naoto
AU - Ozasa, Yuko
AU - Saito, Hideo
N1 - Funding Information:
This work was partially supported by JST CREST “Intelligent Information Processing Systems Creating Co-Experience Knowledge and Wisdom with Human-Machine Harmonious Collaboration.”
Publisher Copyright:
Copyright © 2017 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.
PY - 2017
Y1 - 2017
N2 - Recent years have seen the introduction of service robots as waiters or waitresses in restaurants and cafes. In such venues, it is common for customers to visit in groups as well as for them to engage in conversation while eating and drinking. It is important for cyber serving staff to understand whether they are eating and drinking, or not, in order to wait on tables at appropriate times. In this paper, we present a method by which the robots can recognize eating and drinking actions performed by individuals in a group. Our approach uses the positions of joints in the human body as a feature and long short-term memory to achieve a recognition task on time-series data. We also used head directions in our method, as we assumed that it is effective for recognition in a group. The information garnered from head directions and joint positions is integrated via logistic regression and employed in recognition. The results show that this yielded the highest accuracy and effectiveness of the robots' tasks.
AB - Recent years have seen the introduction of service robots as waiters or waitresses in restaurants and cafes. In such venues, it is common for customers to visit in groups as well as for them to engage in conversation while eating and drinking. It is important for cyber serving staff to understand whether they are eating and drinking, or not, in order to wait on tables at appropriate times. In this paper, we present a method by which the robots can recognize eating and drinking actions performed by individuals in a group. Our approach uses the positions of joints in the human body as a feature and long short-term memory to achieve a recognition task on time-series data. We also used head directions in our method, as we assumed that it is effective for recognition in a group. The information garnered from head directions and joint positions is integrated via logistic regression and employed in recognition. The results show that this yielded the highest accuracy and effectiveness of the robots' tasks.
KW - Action recognition
KW - Information fusion
KW - Long short-term memory
UR - http://www.scopus.com/inward/record.url?scp=85049486434&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85049486434&partnerID=8YFLogxK
U2 - 10.5220/0006200305270533
DO - 10.5220/0006200305270533
M3 - Conference contribution
AN - SCOPUS:85049486434
T3 - ICPRAM 2017 - Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods
SP - 527
EP - 533
BT - ICPRAM 2017 - Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods
A2 - De Marsico, Maria De
A2 - di Baja, Gabriella Sanniti
A2 - Fred, Ana
PB - SciTePress
T2 - 6th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2017
Y2 - 24 February 2017 through 26 February 2017
ER -