Eye Movements Identification
Eye movement detection and simulation
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
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.
- Matlab Implementation
- C++ Reimplementation
- Datasets containing fixations, saccades, as well as straight and circular smooth pursuits
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.