Name
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'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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/978-3-030-66823-5_28 | ECCV Workshops |
Keywords | DocType | Citations |
Behaviour analysis,Pattern discovery,Egocentric vision,Data mining,Lifelogging | Conference | 0 |
PageRank | References | Authors |
0.34 | 3 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Martin Menchon | 1 | 0 | 0.34 |
| Estefanía Talavera | 2 | 27 | 3.41 |
| Jose M. Massa | 3 | 0 | 0.34 |
| P. Radeva | 4 | 115 | 13.89 |