Assessing individual dietary intake in food sharing scenarios with food and human pose detection
File(s)
Accepted version
Author(s)
Lei, Jiabao
Qiu, Jianing
Lo, Frank P-W
Lo, Benny
Type
Conference Paper
Abstract
Food sharing and communal eating are very common in some countries. To assess individual dietary intake in food sharing scenarios, this work proposes a vision-based approach to first capturing the food sharing scenario with a 360-degree camera, and then using a neural network to infer different eating states of each individual based on their body pose and relative positions to the dishes. The number of bites each individual has taken of each dish is then deduced by analyzing the inferred eating states. A new dataset with 14 panoramic food sharing videos was constructed to validate our approach. The results show that our approach is able to reliably predict different eating states as well as individual’s bite count with respect to each dish in food sharing scenarios.
Date Issued
2021-02-21
Date Acceptance
2020-11-12
Citation
2021, pp.549-557
ISBN
9783030688202
ISSN
0302-9743
Publisher
Springer International Publishing
Start Page
549
End Page
557
Copyright Statement
© Springer Nature Switzerland AG 2021. The final publication is available at Springer via https://doi.org/10.1007/978-3-030-68821-9_45
Sponsor
Bill and Melinda Gates Foundation
Bill & Melinda Gates Foundation
British Council (UK)
Identifier
https://link.springer.com/book/10.1007/978-3-030-68821-9
Grant Number
OPP1171395
OPP1171395
IL-NXPO2019/20
Source
6th International Workshop on Multimedia Assisted Dietary Management (MADiMa 2020)
Subjects
Artificial Intelligence & Image Processing
Publication Status
Published
Start Date
2021-01-10
Finish Date
2021-01-15
Coverage Spatial
Virtual event
Date Publish Online
2021-02-21