Visual lifelogging consists of acquiring images that capture the daily experiences of the user by wearing a camera over a long period of time. The pictures taken offer considerable potential for knowledge mining concerning how people live their lives, hence, they open up new opportunities for many potential applications in fields including healthcare, security, leisure and the quantified self. However, automatically building a story from a huge collection of unstructured egocentric data presents major challenges. This paper provides a thorough review of advances made so far in egocentric data analysis, and in view of the current state of the art, indicates new lines of research to move us towards storytelling from visual lifelogging.
@article{arxiv.1507.06120,
title = {Towards Storytelling from Visual Lifelogging: An Overview},
author = {Marc Bolaños and Mariella Dimiccoli and Petia Radeva},
journal= {arXiv preprint arXiv:1507.06120},
year = {2016}
}
Comments
16 pages, 11 figures, Submitted to IEEE Transactions on Human-Machine Systems