%0 Journal Article %1 5b12d7ad277a4f55bcd6bd7f5dea5c72 %A Kurzhals, Kuno %A Hlawatsch, Marcel %A Heimerl, Florian %A Burch, Michael %A Ertl, Thomas %A Weiskopf, Daniel %D 2016 %E IEEE, %J IEEE Transactions on Visualization and Computer Graphics %K 2016 B01 from:leonkokkoliadis sfbtrr161 visus visus:ertl visus:hlawatml visus:kurzhakn visus:weiskopf %N 1 %P 1005-1014 %R 10.1109/TVCG.2015.2468091 %T Gaze Stripes: Image-based Visualization of Eye Tracking Data %U http://dx.doi.org/10.1109/TVCG.2015.2468091 %V 22 %X We present a new visualization approach for displaying eye tracking data from multiple participants. We aim to show the spatio-temporal data of the gaze points in the context of the underlying image or video stimulus without occlusion. Our technique, denoted as gaze stripes, does not require the explicit definition of areas of interest but directly uses the image data around the gaze points, similar to thumbnails for images. A gaze stripe consists of a sequence of such gaze point images, oriented along a horizontal timeline. By displaying multiple aligned gaze stripes, it is possible to analyze and compare the viewing behavior of the participants over time. Since the analysis is carried out directly on the image data, expensive post-processing or manual annotation are not required. Therefore, not only patterns and outliers in the participants' scanpaths can be detected, but the context of the stimulus is available as well. Furthermore, our approach is especially well suited for dynamic stimuli due to the non-aggregated temporal mapping. Complementary views, i.e., markers, notes, screenshots, histograms, and results from automatic clustering, can be added to the visualization to display analysis results. We illustrate the usefulness of our technique on static and dynamic stimuli. Furthermore, we discuss the limitations and scalability of our approach in comparison to established visualization techniques.

@article{5b12d7ad277a4f55bcd6bd7f5dea5c72, abstract = {We present a new visualization approach for displaying eye tracking data from multiple participants. We aim to show the spatio-temporal data of the gaze points in the context of the underlying image or video stimulus without occlusion. Our technique, denoted as gaze stripes, does not require the explicit definition of areas of interest but directly uses the image data around the gaze points, similar to thumbnails for images. A gaze stripe consists of a sequence of such gaze point images, oriented along a horizontal timeline. By displaying multiple aligned gaze stripes, it is possible to analyze and compare the viewing behavior of the participants over time. Since the analysis is carried out directly on the image data, expensive post-processing or manual annotation are not required. Therefore, not only patterns and outliers in the participants' scanpaths can be detected, but the context of the stimulus is available as well. Furthermore, our approach is especially well suited for dynamic stimuli due to the non-aggregated temporal mapping. Complementary views, i.e., markers, notes, screenshots, histograms, and results from automatic clustering, can be added to the visualization to display analysis results. We illustrate the usefulness of our technique on static and dynamic stimuli. Furthermore, we discuss the limitations and scalability of our approach in comparison to established visualization techniques.}, added-at = {2020-10-09T12:31:48.000+0200}, author = {Kurzhals, Kuno and Hlawatsch, Marcel and Heimerl, Florian and Burch, Michael and Ertl, Thomas and Weiskopf, Daniel}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/28c68309eb5c8f6825bb29fc42f7800ed/mueller}, description = {Gaze Stripes: Image-based Visualization of Eye Tracking Data}, doi = {10.1109/TVCG.2015.2468091}, editor = {IEEE}, interhash = {618a2556923eeb8e0f3b91d456a93064}, intrahash = {8c68309eb5c8f6825bb29fc42f7800ed}, journal = {IEEE Transactions on Visualization and Computer Graphics}, keywords = {2016 B01 from:leonkokkoliadis sfbtrr161 visus visus:ertl visus:hlawatml visus:kurzhakn visus:weiskopf}, number = 1, pages = {1005-1014}, timestamp = {2020-10-09T10:31:48.000+0200}, title = {Gaze Stripes: Image-based Visualization of Eye Tracking Data}, url = {http://dx.doi.org/10.1109/TVCG.2015.2468091}, volume = 22, year = 2016 }