Towards Storytelling from Visual Lifelogging: An Overview

TL;DR

This paper reviews recent advances in analyzing egocentric visual data to enable automatic storytelling, highlighting challenges and future research directions in transforming lifelogging images into meaningful narratives.

Contribution

It provides a comprehensive overview of current methods and identifies new research avenues for developing storytelling techniques from visual lifelogging data.

Findings

Review of egocentric data analysis techniques

Identification of key challenges in automatic storytelling

Suggestions for future research directions

Abstract

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.