Analyzing First-Person Stories Based on Socializing, Eating and Sedentary Patterns

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Authors Beatriz Remeseiro, Alberto Soto, Laura Portell, Pedro Herruzo
Journal/Conference Name Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Paper Category
Paper Abstract First-person stories can be analyzed by means of egocentric pictures acquired throughout the whole active day with wearable cameras. This manuscript presents an egocentric dataset with more than 45,000 pictures from four people in different environments such as working or studying. All the images were manually labeled to identify three patterns of interest regarding people's lifestyle socializing, eating and sedentary. Additionally, two different approaches are proposed to classify egocentric images into one of the 12 target categories defined to characterize these three patterns. The approaches are based on machine learning and deep learning techniques, including traditional classifiers and state-of-art convolutional neural networks. The experimental results obtained when applying these methods to the egocentric dataset demonstrated their adequacy for the problem at hand.
Date of publication 2017
Code Programming Language Shell

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