TY - GEN
T1 - EAT - The ICMI 2018 eating analysis and tracking challenge
AU - Hantke, Simone
AU - Tzirakis, Panagiotis
AU - Schmitt, Maximilian
AU - Schuller, Björn
N1 - Publisher Copyright:
© 2018 Association for Computing Machinery.
PY - 2018/10/2
Y1 - 2018/10/2
N2 - The multimodal recognition of eating condition - whether a person is eating or not - and if yes, which food type, is a new research domain in the area of speech and video processing that has many promising applications for future multimodal interfaces such as adapting speech recognition or lip reading systems to different eating conditions. We herein describe the ICMI 2018 Eating Analysis and Tracking (EAT) Challenge and address - for the first time in research competitions under well-defined conditions - new classification tasks in the area of user data analysis, namely audiovisual classifications of user eating conditions. We define three Sub-Challenges based on classification tasks in which participants are encouraged to use speech and/or video recordings of the audiovisual iHEARu-EAT database. In this paper, we describe the dataset, the Sub-Challenges, their conditions, and the baseline feature extraction and performance measures as provided to the participants.
AB - The multimodal recognition of eating condition - whether a person is eating or not - and if yes, which food type, is a new research domain in the area of speech and video processing that has many promising applications for future multimodal interfaces such as adapting speech recognition or lip reading systems to different eating conditions. We herein describe the ICMI 2018 Eating Analysis and Tracking (EAT) Challenge and address - for the first time in research competitions under well-defined conditions - new classification tasks in the area of user data analysis, namely audiovisual classifications of user eating conditions. We define three Sub-Challenges based on classification tasks in which participants are encouraged to use speech and/or video recordings of the audiovisual iHEARu-EAT database. In this paper, we describe the dataset, the Sub-Challenges, their conditions, and the baseline feature extraction and performance measures as provided to the participants.
KW - Challenge
KW - Eating Condition
KW - Human Behaviour
KW - Multimodal Data Analysis
UR - http://www.scopus.com/inward/record.url?scp=85056661893&partnerID=8YFLogxK
U2 - 10.1145/3242969.3243681
DO - 10.1145/3242969.3243681
M3 - Conference contribution
AN - SCOPUS:85056661893
T3 - ICMI 2018 - Proceedings of the 2018 International Conference on Multimodal Interaction
SP - 559
EP - 563
BT - ICMI 2018 - Proceedings of the 2018 International Conference on Multimodal Interaction
PB - Association for Computing Machinery, Inc
T2 - 20th ACM International Conference on Multimodal Interaction, ICMI 2018
Y2 - 16 October 2018 through 20 October 2018
ER -