Towards Storytelling from Visual Lifelogging: An Overview

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.

16 pages, 11 figures, Submitted to IEEE Transactions on Human-Machine Systems

References in corpus (5)

• The Evolution of First Person Vision Methods: A Survey
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• Leveraging Context to Support Automated Food Recognition in Restaurants
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Cited by in corpus (3)

• Analysis of the hands in egocentric vision: A survey
• SR-Clustering: Semantic Regularized Clustering for Egocentric Photo Streams Segmentation
• Foreground-Aware Stylization and Consensus Pseudo-Labeling for Domain Adaptation of First-Person Hand Segmentation