SideWays

Zhang, Yanxia; Bulling, Andreas; Gellersen, Hans

doi:10.1145/2470654.2470775

Cited by 70 publications

(8 citation statements)

References 28 publications

(28 reference statements)

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“…Calibration with a large number of samples can be time-consuming and prohibitive for certain applications where spontaneous interaction is crucial, e.g. on gaze-enabled public displays [74]. Therefore, we evaluated the influence of the number of calibration samples on gaze estimation accuracy.…”

Section: Number Of Calibration Samplesmentioning

confidence: 99%

“…While the attentive user interfaces also requires specific object calibration for each different cameraobject relationship [54], the underlying use case scenario demands pervasive multi-user gaze estimation without personal calibration. For gaze-based user modelling and passive eye monitoring, relative eye movement could be sufficient considering that there have been already applications implemented in a calibration-free fashion [74]. They can be also naturally extended to multi-user scenarios.…”

Section: Gaze Applicationsmentioning

confidence: 99%

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Evaluation of Appearance-Based Methods and Implications for Gaze-Based Applications

Zhang¹,

Sugano

Bulling

2019

Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems

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Appearance-based gaze estimation methods that only require an off-the-shelf camera have significantly improved but they are still not yet widely used in the human-computer interaction (HCI) community. This is partly because it remains unclear how they perform compared to model-based approaches as well as dominant, special-purpose eye tracking equipment. To address this limitation, we evaluate the performance of state-of-the-art appearance-based gaze estimation for interaction scenarios with and without personal calibration, indoors and outdoors, for different sensing distances, as well as for users with and without glasses. We discuss the obtained findings and their implications for the most important gaze-based applications, namely explicit eye input, attentive user interfaces, gaze-based user modelling, and passive eye monitoring. To democratise the use of appearance-based gaze estimation and interaction in HCI, we finally present OpenGaze (www.opengaze.org), the first software toolkit for appearance-based gaze estimation and interaction. CCS CONCEPTS• Computing methodologies → Computer vision.

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Section: Number Of Calibration Samplesmentioning

confidence: 99%

Section: Gaze Applicationsmentioning

confidence: 99%

Evaluation of Appearance-Based Methods and Implications for Gaze-Based Applications

Zhang¹,

Sugano

Bulling

2019

Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems

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“…Dostal and colleagues [2013] addressed similar issues by detecting which monitor the user is looking at through analyzing webcam video. The Sideways system [Zhang 2013] even eliminates personalized calibration and can scroll contents of a display screen by detecting eye gaze. The system identifies whether users are looking at the middle or sides of a display and if they are looking to the sides, the system scrolls content at the middle.…”

Section: Gaze Controlled Interfacementioning

confidence: 99%

Improving Eye Gaze Controlled Car Dashboard using Simulated Annealing

Biswas

Prabhakar

Rajesh

et al. 2017

Electronic Workshops in Computing

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This paper proposes and evaluates a new algorithm to improve performance of pointing and selection tasks in an eye gaze controlled graphical user interface in automotive environment. The algorithm aims to reduce number of wrong selection of targets due to inaccuracy of either the eye gaze tracker or the calibration routine. The proposed method does not require changing layout of the interface rather puts a set of hotspots on clickable targets using Simulated Annealing algorithm. A couple of user studies involving a driving simulator and ISO 26022 lane changing task found that the proposed method can reduce number of wrong selection and can also improve driving performance compared to an existing touchscreen system.

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“…It was necessary to either use a calibration-free technique (e.g. [21,24,18]) or blend the eye tracker calibration into interaction (e.g. [12]).…”

Section: Voting Systemmentioning

confidence: 99%

EyeVote in the wild

Khamis

Trotter

Tessmann

et al. 2016

Proceedings of the 15th International Conference on Mobile and Ubiquitous Multimedia

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Although recovering from errors is straightforward on most interfaces, public display systems pose very unique design challenges. Namely, public display users interact for very short amounts of times and are believed to abandon the display when interrupted or forced to deviate from the main task. To date, it is not well understood whether public display designers should enable users to correct errors (e.g. by asking users to confirm or giving them a chance correct their input), or aim for faster interaction and rely on other types of feedback to estimate errors. To close this gap, we conducted a field study where we investigated the users willingness to correct their input on public displays. We report on our findings from an in-the-wild deployment of a public gaze-based voting system where we intentionally evoked system errors to see if users correct them. We found that public display users are willing to correct system errors provided that the correction is fast and straightforward. We discuss how our findings influence the choice of interaction methods for public displays; interaction methods that are highly usable but suffer from low accuracy can still be effective if users can "undo" their interactions.

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SideWays

Cited by 70 publications

References 28 publications

Evaluation of Appearance-Based Methods and Implications for Gaze-Based Applications

Evaluation of Appearance-Based Methods and Implications for Gaze-Based Applications

Improving Eye Gaze Controlled Car Dashboard using Simulated Annealing

EyeVote in the wild

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