Space-time volume visualization of gaze and stimulus

Our system for visual analysis of gaze and stimulus data.


We present a method for the spatio-temporal analysis of gaze data from multiple participants in the context of a video stimulus. For such data, an overview of the recorded patterns is important to identify common viewing behavior (such as attentional synchrony) and outliers. We adopt the approach of space-time cube visualization, which extends the spatial dimensions of the stimulus by time as the third dimension. Previous work mainly handled eye-tracking data in the space-time cube as point cloud, providing no information about the stimulus context. This paper presents a novel visualization technique that combines gaze data, a dynamic stimulus, and optical flow with volume rendering to derive an overview of the data with contextual information. With specifically designed transfer functions, we emphasize different data aspects, making the visualization suitable for explorative analysis and for illustrative support of statistical findings alike.

Proceedings of the 11th ACM Symposium on Eye Tracking Research & Applications, ETRA 2019, Denver , CO, USA, June 25-28, 2019
Valentin Bruder
Doctoral Researcher

My research interests include scientific visualization, performance modeling/prediction, and GPGPU.