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The Study of Improving the Accuracy of Convolutional Neural Networks in Face Recognition Tasks Read chapter Read first chapter

2021 | OriginalPaper | Chapter

Assessing Individual Dietary Intake in Food Sharing Scenarios with Food and Human Pose Detection

Authors : Jiabao Lei, Jianing Qiu, Frank P.-W. Lo, Benny Lo

Published in: Pattern Recognition. ICPR International Workshops and Challenges

Publisher: Springer International Publishing

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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.

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previous chapter Real-Time Thermal Face Identification System for Low Memory Vision Applications Using CNN
next chapter Recognition of Food-Texture Attributes Using an In-Ear Microphone
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Metadata
Title
Assessing Individual Dietary Intake in Food Sharing Scenarios with Food and Human Pose Detection
Authors
Jiabao Lei
Jianing Qiu
Frank P.-W. Lo
Benny Lo
Copyright Year
2021
Book
Pattern Recognition. ICPR International Workshops and Challenges

Print ISBN: 978-3-030-68820-2

Electronic ISBN: 978-3-030-68821-9

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-68821-9_45

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