EAT - The ICMI 2018 eating analysis and tracking challenge

Simone Hantke, Panagiotis Tzirakis, Maximilian Schmitt, Björn Schuller

Publikation: Beitrag in Buch/Bericht/KonferenzbandKonferenzbeitragBegutachtung

13 Zitate (Scopus)

Abstract

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.

OriginalspracheEnglisch
TitelICMI 2018 - Proceedings of the 2018 International Conference on Multimodal Interaction
Herausgeber (Verlag)Association for Computing Machinery, Inc
Seiten559-563
Seitenumfang5
ISBN (elektronisch)9781450356923
DOIs
PublikationsstatusVeröffentlicht - 2 Okt. 2018
Extern publiziertJa
Veranstaltung20th ACM International Conference on Multimodal Interaction, ICMI 2018 - Boulder, USA/Vereinigte Staaten
Dauer: 16 Okt. 201820 Okt. 2018

Publikationsreihe

NameICMI 2018 - Proceedings of the 2018 International Conference on Multimodal Interaction

Konferenz

Konferenz20th ACM International Conference on Multimodal Interaction, ICMI 2018
Land/GebietUSA/Vereinigte Staaten
OrtBoulder
Zeitraum16/10/1820/10/18

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