A time-efficient re-calibration algorithm for improved long-term accuracy of head-worn eye trackers

Lander, Christian; Kerber, Frederic; Rauber, Thorsten; Krüger, Antonio

doi:10.1145/2857491.2857513

Cited by 10 publications

(5 citation statements)

References 13 publications

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“…Gaze calibration can also deteriorate and become inaccurate over time [Nyström et al 2013]. Work has therefore been done into re-calibration methods that asses the current calibration drift and only re-calibrate the parts that are needed, using smooth pursuits [Gomez and Gellersen 2018] or standard calibration points [Lander et al 2016]. However, while these methods are less timeconsuming than a full calibration, they still require the user to pause their current interaction to perform the needed re-calibration points or movements.…”

Section: Eye Tracking Calibrationmentioning

confidence: 99%

“…Therefore, alternative eye tracking calibration methods has become an extensive research field. Techniques that make the process unobtrusive or gamified [Flatla et al 2011;Gomez and Gellersen 2017;Renner et al 2011], using visual saliency models [Judd et al 2009;Sugano and Bulling 2015], smooth pursuit eye movements [Gomez and Gellersen 2017;Pfeuffer et al 2013;Tripathi and Guenter 2016] or by selecting optimal calibration points have been introduced [Gomez and Gellersen 2018;Lander et al 2016]. However, most commercial eye trackers still use the traditional calibration method due to its reliability.…”

Section: Introductionmentioning

confidence: 99%

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Gaze behaviour on interacted objects during hand interaction in virtual reality for eye tracking calibration

Sidenmark

Lundström

2019

Proceedings of the 11th ACM Symposium on Eye Tracking Research &Amp; Applications

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In this paper, we investigate the probability and timing of attaining gaze fixations on interacted objects during hand interaction in virtual reality, with the main purpose for implicit and continuous eye tracking re-calibration. We conducted an evaluation with 15 participants in which their gaze was recorded while interacting with virtual objects. The data was analysed to find factors influencing the probability of fixations at different phases of interaction for different object types. The results indicate that 1) interacting with stationary objects may be favourable in attaining fixations to moving objects, 2) prolonged and precision-demanding interactions positively influences the probability to attain fixations, 3) performing multiple interactions simultaneously can negatively impact the probability of fixations, and 4) feedback can initiate and end fixations on objects. CCS CONCEPTS • Human-centered computing → Empirical studies in HCI; Virtual reality.

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Section: Eye Tracking Calibrationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Gaze behaviour on interacted objects during hand interaction in virtual reality for eye tracking calibration

Sidenmark

Lundström

2019

Proceedings of the 11th ACM Symposium on Eye Tracking Research &Amp; Applications

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“…This mapping has to be renewed on a regular basis to keep a constant highly accurate gaze estimation over time [12]. Approaches utilizing visual markers [2] in the environment or detecting surfaces (e.g., displays) directly in the scene camera's frame [43] allow for gaze estimation on specific interaction areas.…”

Section: Eye Tracking and Gaze Estimationmentioning

confidence: 99%

“…Although current commercial devices (e.g., Tobii Pro Glasses [39]) are built on model-based (geometric) gaze estimation methods, they require at least a one-point calibration, and more to increase its accuracy [45]. The problem of calibration drift [12], caused by different factors (e.g., eye physiology, environmental factors, er the device's position), worsens gaze estimation over time. Hence a periodic re-calibration is mandatory.…”

Section: Introductionmentioning

confidence: 99%

Eyemirror

Lander

Gehring

Löchtefeld

et al. 2017

Proceedings of the 16th International Conference on Mobile and Ubiquitous Multimedia

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Gaze is a powerful measure of people's attracted attention and reveals where we are looking within our current FOV. Hence gaze-based interfaces are gaining in importance. However, gaze estimation usually requires extensive hardware and depends on a calibration that has to be renewed regularly. We present EyeMirror, a mobile device for calibration-free gaze approximation on surfaces (e.g., displays). It consists of a head-mounted camera, connected to a wearable minicomputer, capturing the environment reflected on the human cornea. The corneal images are analyzed using natural feature tracking for gaze estimation on surfaces. In two lab studies we compared variations of EyeMirror against established methods for gaze estimation in a display scenario, and investigated the effect of display content (i.e. number of features). EyeMirror achieved 4.03°gaze estimation error, while we found no significant effect of display content.

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“…To keep gaze data quality high, recalibration in suitable intervals is required; however, conducted like the initial calibration it would be annoying for the user [HH02]. [LKRK16] show that cute spreading of the recalibration points can reduce the number to only two. However, it seems more compelling to utilize targets provided on the screen during the working task for unobtrusive recalibration.…”

Section: Introduction and Related Workmentioning

confidence: 99%

An Analysis of Accuracy Requirements for Automatic Eyetracker Recalibration at Runtime

Camp

Gill

Hild

et al. 2016

HCI International 2016 – Posters' Extended Abstracts

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The initial calibration of an eye-tracker is a crucial step to provide accurate gaze data, often as a position on a screen. Issues influencing the calibration such as the user's pose can change while using the eye tracker. Hence, recalibration might often be necessary but at the expense of interrupting the user executing the working task. Monitoring interactions such as clicks on a target or detecting salient objects could provide recalibration points without deliberate user interaction. To gain insight into how accurate recalibration points must be localized to ensure that gaze estimation accuracy is improved, we conducted a user study and examined the effect of correct as well as erroneous localization of recalibration points. The results show that even a localization error of 1.2 degrees of visual angle induces an error of less than 0.5 • to the estimated gaze position on screen. Our results indicate the necessary requirements any method automatically providing recalibration points has to fulfill.

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A time-efficient re-calibration algorithm for improved long-term accuracy of head-worn eye trackers

Cited by 10 publications

References 13 publications

Gaze behaviour on interacted objects during hand interaction in virtual reality for eye tracking calibration

Gaze behaviour on interacted objects during hand interaction in virtual reality for eye tracking calibration

Eyemirror

An Analysis of Accuracy Requirements for Automatic Eyetracker Recalibration at Runtime

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