Human Computer Interaction

Eye Movements Identification

Eye movement detection and simulation

Description Simulator

Description Histogram of oriented velocities

Description Rule lerner

Sourcecode and lib downloads

  • Simulator Matlab files
  • Histogram of oriented velocities C++ lib
  • Histogram of oriented velocities Matlab files
  • Rule lerner C++ lib

Password: eyedata
User: emmapupildata
All algorithms

The simulator and all detection algorithms are also integrated into EyeTrace


Automatic Identification of Eye Movements


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.


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.



Bayesian Identification of Fixations, Saccades, and Smooth Pursuits

by T. Santini, W. Fuhl, T. C. Kübler, and E. Kasneci

In Proceedings of the Ninth Biennial ACM Symposium on Eye Tracking Research & Applications (ETRA), pages 163–170, 2016.


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Bayesian Online Clustering of Eye-Tracking Data

by E. Tafaj, G. Kasneci, W. Rosenstiel, and M. Bogdan

In Eye Tracking Research and Applications (ETRA 2012), 2012.


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