3D gaze estimation from 2D pupil positions on monocular head-mounted eye trackers

Mansouryar, Mohsen; Steil, Julian; Sugano, Yusuke; Bulling, Andreas

doi:10.1145/2857491.2857530

Cited by 44 publications

(29 citation statements)

References 12 publications

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“…Mapping gaze onto a 3D target (e.g. a sculpture) is a more computationally demanding challenge, but could be achieved by combining 3D reconstruction algorithms (Moons, Van Gool, and Vergauwen 2010;Lepetit and Fua 2005) with methods for estimating participants' gaze depth (Mansouryar et al 2016;Lee et al 2012) . Furthermore, ideal targets should contain non-symmetric, unique elements with relatively high-spatial frequency information, in order to ensure a sufficient set of keypoints can be identified on the reference image.…”

Section: Dynamic Gaze Mappingmentioning

confidence: 99%

Wearable Eye-tracking for Research: Automated dynamic gaze mapping and accuracy/precision comparisons across devices

Iqbal

2018

Preprint

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Wearable eye-trackers offer exciting advantages over screen-based systems, but their use in research settings has been hindered by significant analytic challenges as well as a lack of published performance measures among competing devices on the market. In this article, we address both of these limitations. We describe (and make freely available) an automated analysis pipeline for mapping gaze data from an egocentric coordinate system (i.e. the wearable eye-tracker) to a fixed reference coordinate system (i.e. a target stimulus in the environment). This pipeline allows researchers to study aggregate viewing behavior on a 2D planar target stimulus without restricting the mobility of participants. We also designed a task to directly compare calibration accuracy and precision across 3 popular models of wearable eye-trackers: Pupil Labs 120Hz Binocular glasses, SMI ETG 2 glasses, and the Tobii Pro Glasses 2. Our task encompassed multiple viewing conditions selected to approximate distances and gaze angles typical for short-to mid-range viewing experiments. This work will promote and facilitate the use of wearable eye-trackers for research in naturalistic viewing experiments.All analyses were completed using R v3.4.0 (R Core Team 2017) , with data formatting using the dplyr package (Wickham et al. 2017) . All linear mixed effects models were performed using the lme4 package (Bates et al. 2015) , and follow-up pairwise comparisons were performed using the lsmeans package (Lenth and Others 2016) . All results plots were created using ggplot2 (Wickham 2009) , ggpubr (Kassambara 2017) , and ggsignif (Ahlmann-Eltze 2017) packages for R . Overall PerformanceThe percentage of removed outlier (>5° from target location) gaze points during preprocessing differed by eye-tracker model. Statistical comparisons revealed the mean percentage of valid gaze points for Pupil Labs (mean: 97.1%; SE: 0.9) was significantly lower than SMI (mean: 98.7%; SE: 0.4) and Tobii (mean: 98.2%; SE: 0.4) (p < 0.001; no significant difference between Tobii and SMI).However, given that all models retained > 97% of all gaze points, this difference had a negligible effect on interpretation of subsequent analyses.We first averaged across all distances and gaze angle conditions, and tested the overall relationships between eye-tracker model and accuracy, and eye-tracker model and precision. AccuracyWe fit a linear mixed effects model to test the relationship between accuracy and eye-tracker. This model included eye-tracker as a fixed effect and subject as a random effect. Eye-tracker was a significant predictor of accuracy ( F (2,78) = 7.44, p < .001) in this model. Follow-up pairwise comparisons between eye-trackers revealed that the Pupil Labs eye-tracker was significantly more accurate than SMI ( t (78) = 2.40, p < .05) and Tobii ( t (78) = 3.81, p < .001). All other comparisons were non-significant at p > .1; see Table 2 , and Fig 4.A .

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Section: Dynamic Gaze Mappingmentioning

confidence: 99%

Wearable Eye-tracking for Research: Automated dynamic gaze mapping and accuracy/precision comparisons across devices

Iqbal

2018

Preprint

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“…Then gaze directions are represented as the eyeball pseudo-center and gaze vectors. Based on a simplified eye model of a perfect sphere where optical axes coincide with visual axes, back-projection methods [14], [23] estimate the eyeball center and gaze vectors in the coordinate system of the eye camera by back-projecting pupil image ellipses. Transforming the coordinate system into the scene camera introduces six parameters of homogeneous transformation.…”

Section: A Gaze Direction Estimation Using the Eyeball Pseudo-centermentioning

confidence: 99%

“…To avoid confusion, we adopt the eyeball pseudo-center to represent the intersection of all gaze directions. Gaze directions are represented as the eyeball pseudo-center and gaze vectors [13], [14] or 2D mapping points on the imaginary mapping plane [10]. The existing regression-based methods have two limitations that affect the accuracy of 3D gaze estimation.…”

Section: Introductionmentioning

confidence: 99%

Accurate Regression-Based 3D Gaze Estimation Using Multiple Mapping Surfaces

Wan

Chen

et al. 2020

IEEE Access

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Accurate 3D gaze estimation using a simple setup remains a challenging issue for headmounted eye tracking. Current regression-based gaze direction estimation methods implicitly assume that all gaze directions intersect at one point called the eyeball pseudo-center. The effect of this implicit assumption on gaze estimation is unknown. In this paper, we find that this assumption is approximate based on a simulation of all intersections of gaze directions, and it is conditional based on a sensitivity analysis of the assumption in gaze estimation. Hence, we propose a gaze direction estimation method with one mapping surface that satisfies conditions of the assumption by configuring one mapping surface and achieving a highquality calibration of the eyeball pseudo-center. This method only adds two additional calibration points outside the mapping surface. Furthermore, replacing the eyeball pseudo-center with an additional calibrated surface, we propose a gaze direction estimation method with two mapping surfaces that further improves the accuracy of gaze estimation. This method improves accuracy on the state-of-the-art method by 20 percent (from a mean error of 1.84 degrees to 1.48 degrees) on a public dataset with a usage range of 1 meter and by 17 percent (from a mean error of 2.22 degrees to 1.85 degrees) on a public dataset with a usage range of 2 meters.

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“…The literature of using eye and/or head gaze for attention estimation is vast and spans many years. Recently, standard methods to detect gaze directions commonly involve eye trackers [8] or determining head orientation [9], [10]. The information about both head orientation and eye gaze has been linked to a person's focus of attention in the past [11], [12].…”

Section: Behaviour Cues For Attention Estimationmentioning

confidence: 99%

I Can See Your Aim: Estimating User Attention from Gaze for Handheld Robot Collaboration

Stolzenwald

Mayol-Cuevas

2018

2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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This paper explores the estimation of user attention in the setting of a cooperative handheld robot -a robot designed to behave as a handheld tool but that has levels of task knowledge. We use a tool-mounted gaze tracking system, which after modelling via a pilot study, we use as a proxy for estimating the attention of the user. This information is then used for cooperation with users in a task of selecting and engaging with objects on a dynamic screen. Via a video game setup, we test various degrees of robot autonomy from fully autonomous, where the robot knows what it has to do and acts, to no autonomy where the user is in full control of the task. Our results measure performance and subjective metrics and show how the attention model benefits the interaction and preference of users.

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3D gaze estimation from 2D pupil positions on monocular head-mounted eye trackers

Cited by 44 publications

References 12 publications

Wearable Eye-tracking for Research: Automated dynamic gaze mapping and accuracy/precision comparisons across devices

Wearable Eye-tracking for Research: Automated dynamic gaze mapping and accuracy/precision comparisons across devices

Accurate Regression-Based 3D Gaze Estimation Using Multiple Mapping Surfaces

I Can See Your Aim: Estimating User Attention from Gaze for Handheld Robot Collaboration

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