Analysis of people’s nutrition habits is one of the most important mechanisms for applying a thorough monitorisation of several medical conditions (e.g. diabetes, obesity, etc.) that affect a high percentage of the global population. Methods for automatically logging one’s meals could not only make the process easier, but also make it objective to the user’s point of view and interpretability. One of the solutions adopted recently that could ease the automatic construction of nutrition diaries is to ask individuals to take photos with their mobile phones. An alternative technique is visual lifelogging that consists of using a wearable camera that automatically captures pictures from the user point of view (egocentric point of view) with the aim to analyse different patterns of his/her daily life and extract highly relevant information like nutritional habits. In this work we explore how deep learning applied to the food detection and food recognition problems can help to automatically infer the user’s eating pattern.
Food Analysis Demo
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- Petia Radeva: Can Deep Learning and Egocentric Vision for Visual Lifelogging Help Us Eat Better? CCIA2016: 4
- Marc Bolaños, Maite Garolera, Petia Radeva: Active labeling application applied to food-related object recognition. CEA@ACM Multimedia 2013: 45-50