@InProceedings{MartinMenchon2020,
author="Martin Menchon
and Estefania Talavera
and Jose M. Massa
and Petia Radeva",
title="Behavioural Pattern Discovery from Collections of Egocentric Photo-Streams",
booktitle="ECCV Workshops",
year="2020",
volume="12538",
pages="469--484",
abstract="The automatic discovery of behaviour is of high importance when aiming to assess and improve the quality of life of people. Egocentric images offer a rich and objective description of the daily life of the camera wearer. This work proposes a new method to identify a person{\textquoteright}s patterns of behaviour from collected egocentric photo-streams. Our model characterizes time-frames based on the context (place, activities and environment objects) that define the images composition. Based on the similarity among the time-frames that describe the collected days for a user, we propose a new unsupervised greedy method to discover the behavioural pattern set based on a novel semantic clustering approach. Moreover, we present a new score metric to evaluate the performance of the proposed algorithm. We validate our method on 104 days and more than 100k images extracted from 7 users. Results show that behavioural patterns can be discovered to characterize the routine of individuals and consequently their lifestyle.",
optnote="MILAB; no proj",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=3528), last updated on Sat, 05 Mar 2022 00:17:05 +0100",
opturl="https://doi.org/10.1007/978-3-030-66823-5_28",
file=":http://refbase.cvc.uab.es/files/MTM2020.pdf:PDF"
}