Towards Accurate Eye Tracker Calibration – Methods and Procedures

Harężlak, Katarzyna; Kasprowski, Paweł; Stasch, Mateusz

doi:10.1016/j.procs.2014.08.194

Cited by 52 publications

(22 citation statements)

References 12 publications

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“…For using the gaze data evaluation methods, first of all, a sample of gaze data from one or more participants is required [8]. To collect gaze data, (a) a user has to sit in front of the eye tracker under test, which is mounted on a computer screen, and the user eyes are calibrated [49]. (b) The user is presented with visual stimuli, and the eye tracker under test should record the gaze coordinates of the user as the user gazes at the stimuli points.…”

Section: Steps For Using the Gaze Data Evaluation Methods In Gazevisumentioning

confidence: 99%

Development of Open-source Software and Gaze Data Repositories for Performance Evaluation of Eye Tracking Systems

Kar

Corcoran

2019

Vision

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In this paper, a range of open-source tools, datasets, and software that have been developed for quantitative and in-depth evaluation of eye gaze data quality are presented. Eye tracking systems in contemporary vision research and applications face major challenges due to variable operating conditions such as user distance, head pose, and movements of the eye tracker platform. However, there is a lack of open-source tools and datasets that could be used for quantitatively evaluating an eye tracker’s data quality, comparing performance of multiple trackers, or studying the impact of various operating conditions on a tracker’s accuracy. To address these issues, an open-source code repository named GazeVisual-Lib is developed that contains a number of algorithms, visualizations, and software tools for detailed and quantitative analysis of an eye tracker’s performance and data quality. In addition, a new labelled eye gaze dataset that is collected from multiple user platforms and operating conditions is presented in an open data repository for benchmark comparison of gaze data from different eye tracking systems. The paper presents the concept, development, and organization of these two repositories that are envisioned to improve the performance analysis and reliability of eye tracking systems.

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Section: Steps For Using the Gaze Data Evaluation Methods In Gazevisumentioning

confidence: 99%

Development of Open-source Software and Gaze Data Repositories for Performance Evaluation of Eye Tracking Systems

Kar

Corcoran

2019

Vision

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“…We first learn a model p ← f h (h) that maps an angular position of the participant head (h) into a 2D position on the table (p) where the participant is looking at. Following the guidelines of the device [8], we calibrated it as an eye-tracker by training a Support Vector Regression (SVR) [19] with a RBF kernel (C=10, gamma=5) on 14 known points on the table. Participants were asked to look at each point for a duration of 1950 ms out of which the first 700 ms period was ignored.…”

Section: Head Movementmentioning

confidence: 99%

Exploring Temporal Dependencies in Multimodal Referring Expressions with Mixed Reality

Sibirtseva

Ghadirzadeh

Leite

et al. 2019

Virtual, Augmented and Mixed Reality. Applications and Case Studies

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In collaborative tasks, people rely both on verbal and nonverbal cues simultaneously to communicate with each other. For humanrobot interaction to run smoothly and naturally, a robot should be equipped with the ability to robustly disambiguate referring expressions. In this work, we propose a model that can disambiguate multimodal fetching requests using modalities such as head movements, hand gestures, and speech. We analysed the acquired data from mixed reality experiments and formulated a hypothesis that modelling temporal dependencies of events in these three modalities increases the model's predictive power. We evaluated our model on a Bayesian framework to interpret referring expressions with and without exploiting the temporal prior.

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“…In any case, the eye tracker has to be carefully timesynchronised with the stimulus presentation to accurately capture eye movements. This is particularly important if accuracy at the speed of saccadic eye movements is needed (Harezlak et al 2014). To assure spatial accuracy, eye trackers have to be carefully calibrated to each individual observer using pre-defined calibration patterns.…”

Section: Gaze Trackingmentioning

confidence: 99%

Extraction and investigation of biosignal features for visual discomfort evaluation

Abromavičius¹

2019

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Vadovas prof. dr. Artūras SERACKIS (Vilniaus Gedimino technikos universitetas, elektros ir elektronikos inžinerija-T 001). Vilniaus Gedimino technikos universiteto Elektros ir elektronikos inžinerijos mokslo krypties disertacijos gynimo taryba: Pirmininkas prof. habil. dr. Romanas MARTAVIČIUS (Vilniaus Gedimino technikos universitetas, elektros ir elektronikos inžinerija-T 001). Nariai: prof. dr. Adriano DE OLIVEIRA ANDRADE (Federalinis Uberlandijos universitetas, Brazilija, elektros ir elektronikos inžinerija-T 001), dr. Rytis MASKELIŪNAS (Kauno technologijos universitetas, informatikos inžinerija-T 007), doc. dr. Vitalij NOVICKIJ (Vilniaus Gedimino technikos universitetas, elektros ir elektronikos inžinerija-T 001), prof. dr. Nerija ŽURAUSKIENĖ (Valstybinis mokslinių tyrimų institutas Fizinių ir technologijos mokslų centras, elektros ir elektronikos inžinerija-T 001). Disertacija bus ginama viešame Elektros ir elektronikos inžinerijos mokslo krypties disertacijos gynimo tarybos posėdyje 2019 m. rugsėjo 16 d. 14 val. Vilniaus Gedimino technikos universiteto senato posėdžių salėje.

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Towards Accurate Eye Tracker Calibration – Methods and Procedures

Cited by 52 publications

References 12 publications

Development of Open-source Software and Gaze Data Repositories for Performance Evaluation of Eye Tracking Systems

Development of Open-source Software and Gaze Data Repositories for Performance Evaluation of Eye Tracking Systems

Exploring Temporal Dependencies in Multimodal Referring Expressions with Mixed Reality

Extraction and investigation of biosignal features for visual discomfort evaluation

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