Eye tracking data in multimedia containers for instantaneous visualizations

Schöning, Julius; Faion, Patrick; Heidemann, Gunther; Krumnack, Ulf

doi:10.1109/etvis.2016.7851171

Cited by 4 publications

(2 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To motivate the VA software designers also to develop VA add-ons based on existing software, we provide two VLC player add-ons, which allow the reasoning if, e.g., two gaze trajectories of two different subjects are focused on the same object of interest. For the future, we plan to expand VA add-ons to support almost every kind of scientific metadata and to use the insights gained for bio-inspired, as well as, sensory-improved computer vision ( 19 ).…”

Section: Discussionmentioning

confidence: 99%

“…Further, VA on gaze data may experience a boost once a standard format exists that allows sharing data and results, and combining gaze data with other metadata. Building on previous work on merging video and eyetracking data (Schöning, Faion, Heidemann, and Krumnack, 2016), we focus on the following aspects in the present paper: i) a more detailed discussion of feasible data formats for metadata description, timed data representation, and multimedia encapsulation (cf. Section Data Formats and Suitable Data Formats for Gaze Data), ii) the description and implementation of software tools for creating and decomposing multimedia containers with gaze data as well as our first two add-ons for VA on gaze data using the VLC media player (cf.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Visual Analytics of Gaze Data with Standard Multimedia Player

Schöning

Gundler

Heidemann

et al. 2017

JEMR

Self Cite

View full text Add to dashboard Cite

With the increasing number of studies, where participants’ eye movements are tracked while watching videos, the volume of gaze data records is growing tremendously. Unfortunately, in most cases, such data are collected in separate files in custom-made or proprietary data formats. These data are difficult to access even for experts and effectively inaccessible for non-experts. Normally expensive or custom-made software is necessary for their analysis. We address this problem by using existing multimedia container formats for distributing and archiving eye-tracking and gaze data bundled with the stimuli data. We define an exchange format that can be interpreted by standard multimedia players and can be streamed via the Internet. We convert several gaze data sets into our format, demonstrating the feasibility of our approach and allowing to visualize these data with standard multimedia players. We also introduce two VLC player add-ons, allowing for further visual analytics. We discuss the benefit of gaze data in a multimedia container and explain possible visual analytics approaches based on our implementations, converted datasets, and first user interviews.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%