%0 Conference Proceedings
%T Behavioural Pattern Discovery from Collections of Egocentric Photo-Streams
%A Martin Menchon
%A Estefania Talavera
%A Jose M. Massa
%A Petia Radeva
%B ECCV Workshops
%D 2020
%V 12538
%F Martin Menchon2020
%O MILAB; no proj
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=3528), last updated on Sat, 05 Mar 2022 00:17:05 +0100
%X 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’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.
%U https://doi.org/10.1007/978-3-030-66823-5_28
%U http://refbase.cvc.uab.es/files/MTM2020.pdf
%P 469-484