Smooth-i:smart re-calibration using smooth pursuit eye movements

Ramirez Gomez, Argenis and Gellersen, Hans (2018) Smooth-i:smart re-calibration using smooth pursuit eye movements. In: ETRA '18 Proceedings of the 2018 ACM Symposium on Eye Tracking Research & Applications. ACM. ISBN 9781450357067

PDF (Smoothi_CameraReady)
Smoothi_CameraReady.pdf - Accepted Version
Available under License Creative Commons Attribution-NonCommercial.

Download (559kB)


Eye gaze for interaction is dependent on calibration. However, gaze calibration can deteriorate over time affecting the usability of the system. We propose to use motion matching of smooth pursuit eye movements and known motion on the display to determine when there is a drift in accuracy and use it as input for re-calibration. To explore this idea we developed Smooth-i, an algorithm that stores calibration points and updates them incrementally when inaccuracies are identified. To validate the accuracy of Smooth-i, we conducted a study with five participants and a remote eye tracker. A baseline calibration profile was used by all participants to test the accuracy of the Smooth-i re-calibration following interaction with moving targets. Results show that Smooth-i is able to manage re-calibration efficiently, updating the calibration profile only when inaccurate data samples are detected.

Item Type:
Contribution in Book/Report/Proceedings
Additional Information:
© ACM, 2018. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ETRA '18 Proceedings of the 2018 ACM Symposium on Eye Tracking Research & Applications
ID Code:
Deposited By:
Deposited On:
10 Aug 2018 13:24
Last Modified:
26 Jan 2023 02:08