Automatic Identification of Eye Movements

I-BDT

The Bayesian Decision Theory Identification (I-BDT) algorithm was designed to identify fixations, saccades, and smooth pursuits in real-time for low-resolution eye trackers. Additionally, the algorithm operates directly on the eye-position signal and, thus, requires no calibration.

Access:

 

Citation:

T. Santini, W. Fuhl, T. K├╝bler, and E. Kasneci. Bayesian Identification of Fixations, Saccades, and Smooth Pursuits ACM Symposium on Eye Tracking Research & Applications, ETRA 2016.

Example:

Bayesian Online Clustering of Eye Movements

The task of automatically tracking the visual attention in dynamic visual scenes is highly challenging. To approach it, we propose a Bayesian online learning algorithm. As the visual scene changes and new objects appear, based on a mixture model, the algorithm can identify and tell saccades from visual fixations.

The source code for use with Visual Studio is included in the ScanpathViewer Software. Scanpath Viewer is a visualization tool for eye-tracking recordings. It can produce customizable, animated heatmaps and scanpath graphs.

www-ti.informatik.uni-tuebingen.de/~kueblert/SubsMatch1.0.zip

 

Tafaj, E., Kasneci, G., Rosenstiel, W., & Bogdan, M. (2012). Bayesian online clustering of eye-tracking data. In Eye Tracking Research and Applications (ETRA 2012).