Behavioural pattern discovery from collections of egocentric photo-streams
This work addresses the problem of behavior assessment for quality-of-life improvement, but it is incremental as it builds on existing methods for egocentric image analysis.
The authors tackled the problem of automatically discovering behavioral patterns from egocentric photo-streams to assess quality of life, proposing a new unsupervised greedy method with a novel semantic clustering approach and validating it on 104 days and over 100k images from 7 users, showing that patterns can characterize individual routines and lifestyles.
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.