Egocentric Image Captioning for Privacy-Preserved Passive Dietary Intake Monitoring

TL;DR

This paper introduces a privacy-preserving egocentric image captioning method for dietary monitoring, converting images into descriptive text to assess intake while safeguarding privacy in real-world settings.

Contribution

It presents a novel transformer-based architecture for egocentric dietary image captioning and creates a new dataset for real-life dietary assessment.

Findings

Effective in converting dietary images to descriptive captions

Reduces privacy risks by avoiding direct image sharing

First application of image captioning for dietary assessment in real-world scenarios

Abstract

Camera-based passive dietary intake monitoring is able to continuously capture the eating episodes of a subject, recording rich visual information, such as the type and volume of food being consumed, as well as the eating behaviours of the subject. However, there currently is no method that is able to incorporate these visual clues and provide a comprehensive context of dietary intake from passive recording (e.g., is the subject sharing food with others, what food the subject is eating, and how much food is left in the bowl). On the other hand, privacy is a major concern while egocentric wearable cameras are used for capturing. In this paper, we propose a privacy-preserved secure solution (i.e., egocentric image captioning) for dietary assessment with passive monitoring, which unifies food recognition, volume estimation, and scene understanding. By converting images into rich text descriptions, nutritionists can assess individual dietary intake based on the captions instead of the original images, reducing the risk of privacy leakage from images. To this end, an egocentric dietary image captioning dataset has been built, which consists of in-the-wild images captured by head-worn and chest-worn cameras in field studies in Ghana. A novel transformer-based architecture is designed to caption egocentric dietary images. Comprehensive experiments have been conducted to evaluate the effectiveness and to justify the design of the proposed architecture for egocentric dietary image captioning. To the best of our knowledge, this is the first work that applies image captioning for dietary intake assessment in real life settings.