Eye Tracking for Personal Visual Analytics.

Abstract

In many research fields, eye tracking has become an established method to analyze the distribution of visual attention in various scenarios. With the trend toward increasingly affordable and easy-to-use consumer hardware, we expect mobile eye tracking to become ubiquitous, recording massive amounts of gaze data on a regular basis in everyday personal situations. To make use of this data, new approaches for personal visual analytics will be necessary to make the data accessible for non-expert users for self-reflection and re-experiencing interesting events. We discuss how eye tracking fits in the context of personal visual analytics, the challenges that arise with its application to everyday situations, and the research perspectives of personal eye tracking. Therefore, the extraction and representation of areas of interest (AOIs) in the recorded data is a crucial part of data processing. We present a new technique to represent these AOIs from multiple videos: the AOI cloud. In our example, we apply this technique to examine the personal encounters of a user with other persons. The technique provides an accessible user interface that is also applicable to touch devices and therefore suitable for an integration into the everyday life of a user.