Proceedings: GI 2015

AOI transition trees

Kuno Kurzhals , Daniel Weiskopf

Proceedings of Graphics Interface 2015: Halifax, Nova Scotia, Canada, 3 - 5 June 2015, 41-48

DOI 10.20380/GI2015.06

  • Bibtex

    @inproceedings{Kurzhals:2015:10.20380/GI2015.06,
    author = {Kurzhals, Kuno and Weiskopf, Daniel},
    title = {AOI transition trees},
    booktitle = {Proceedings of Graphics Interface 2015},
    series = {GI 2015},
    year = {2015},
    issn = {0713-5424},
    isbn = {978-1-4822-6003-8},
    location = {Halifax, Nova Scotia, Canada},
    pages = {41--48},
    numpages = {8},
    doi = {10.20380/GI2015.06},
    publisher = {Canadian Human-Computer Communications Society},
    address = {Toronto, Ontario, Canada},
    }

Abstract

The analysis of transitions between areas of interest (AOIs) in eye tracking data provides insight into visual reading strategies followed by participants. We present a new approach to investigate eye tracking data of multiple participants, recorded from video stimuli. Our new transition trees summarize sequence patterns of all participants over complete videos. Shot boundary information from the video is used to divide the dynamic eye tracking information into time spans of similar semantics. AOI transitions within such a time span are modeled as a tree and visualized by an extended icicle plot that shows transition patterns and frequencies of transitions. Thumbnails represent AOIs in the visualization and allow for an interpretation of AOIs and transitions between them without detailed knowledge of the video stimulus. A sequence of several shots is visualized by connecting the respective icicle plots with curved links that indicate the correspondence of AOIs. We compare the technique with other approaches that visualize AOI transitions. With our approach, common transition patterns in eye tracking data recorded for several participants can be identified easily. In our use case, we demonstrate the scalability of our approach concerning the number of participants and investigate a video data set with the transition tree visualization.