Eye movement detection and simulationDescription Simulator: Description Histogram of oriented velocities: Description Rule lerner: Sourcecode and lib downloads:
User:emmapupildata All algorithms (ftp) The simulator and all detection algorithms are also integrated into EyeTrace Automatic Identification of Eye MovementsI-BDTThe 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. Kübler, 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 MovementsThe 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). |
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