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
All algorithms (ftp)

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.




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.


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.



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