Behaviour understanding through the analysis of image sequences collected by wearable cameras

Estefanía Talavera Martínez

Abstract

Describing people's lifestyle has become a hot topic in the field of artificial intelligence. Lifelogging is described as the process of collecting personal activity data describing the daily behaviour of a person. Nowadays, the development of new technologies and the increasing use of wearable sensors allow to automatically record data from our daily living. In this paper, we describe our developed automatic tools for the analysis of collected visual data that describes the daily behaviour of a person. For this analysis, we rely on sequences of images collected by wearable cameras, which are called egocentric photo-streams. These images are a rich source of information about the behaviour of the camera wearer since they show an objective and first-person view of his or her lifestyle.

Keywords

Computer Vision, Egocentric Vision, Lifelogging, Temporal Segmentation, Behaviour Understanding, Lifestyle Tracking, Food-scenes classification, Sentiment Retrieval, Social patterns, Visual Pattern Recognition, Scene Understanding

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Copyright (c) 2020 Estefanía Talavera Martínez