Name
Abstract | ||
|---|---|---|
One of the obstacles to bring eye tracking technology to everyday human computer interactions is the time consuming calibration procedure. In this paper we investigate a novel calibration method based on smooth pursuit eye movement. The method uses linear regression to calculate the calibration mapping. The advantage is that users can perform the calibration quickly in a few seconds and only use a small calibration area to cover a large tracking area. We first describe the theoretical background on establishing a calibration mapping and discuss differences of calibration methods used. We then present a user study comparing the new regression-based method with a classical nine-point and with other pursuit-based calibrations. The results show the proposed method is fully functional, quick, and enables accurate tracking of a large area. The method has the potential to be integrated into current eye tracking systems to make them more usable in various use cases.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3314111.3319818 | Proceedings of the 11th ACM Symposium on Eye Tracking Research & Applications |
Keywords | Field | DocType |
eye tracker calibration, linear regression, smooth pursuits | Smooth pursuit,USable,Computer vision,Use case,Regression,Computer science,Spacetime,Eye tracking,Artificial intelligence,Calibration,Linear regression | Conference |
ISBN | Citations | PageRank |
978-1-4503-6709-7 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Heiko Drewes | 1 | 2 | 1.37 |
| Ken Pfeuffer | 2 | 172 | 14.64 |
| Florian Alt | 3 | 1552 | 119.24 |