TY  - CONF
AU  - Martin Menchon
AU  - Estefania Talavera
AU  - Jose M. Massa
AU  - Petia Radeva
A2  - ECCVW
PY  - 2020//
TI  - Behavioural Pattern Discovery from Collections of Egocentric Photo-Streams
T2  - LNCS
BT  - ECCV Workshops
SP  - 469
EP  - 484
VL  - 12538
N2  - 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.
UR  - https://doi.org/10.1007/978-3-030-66823-5_28
L1  - http://refbase.cvc.uab.es/files/MTM2020.pdf
N1  - MILAB; no proj
ID  - Martin Menchon2020
ER  -