N. Gunawardena, J. A. Ginige, Bahman Javadi, G. Lui
Eye-tracking has been used in various domains, including human-computer interaction, psychology, and many others. Compared to commercial eye trackers, eye tracking using off-the-shelf cameras has many advantages, such as lower cost, pervasiveness, and mobility. Quantifying human attention on the mobile device is invaluable in human-computer interaction. Like videos and mobile games, dynamic visual stimuli require higher attention than static visual stimuli such as web pages and images. This research aims to develop an accurate eye-tracking algorithm using the front-facing camera of mobile devices to identify human attention hotspots when viewing video type contents. The shortage of computational power in mobile devices becomes a challenge to obtain higher user satisfaction. Edge computing moves the processing power closer to the source of the data and reduces the latency introduced by the cloud computing. Therefore, the proposed algorithm will be extended with mobile edge computing to provide a real-time eye tracking experience for users
{"title":"Mobile Device Eye Tracking on Dynamic Visual Contents using Edge Computing and Deep Learning","authors":"N. Gunawardena, J. A. Ginige, Bahman Javadi, G. Lui","doi":"10.1145/3517031.3532198","DOIUrl":"https://doi.org/10.1145/3517031.3532198","url":null,"abstract":"Eye-tracking has been used in various domains, including human-computer interaction, psychology, and many others. Compared to commercial eye trackers, eye tracking using off-the-shelf cameras has many advantages, such as lower cost, pervasiveness, and mobility. Quantifying human attention on the mobile device is invaluable in human-computer interaction. Like videos and mobile games, dynamic visual stimuli require higher attention than static visual stimuli such as web pages and images. This research aims to develop an accurate eye-tracking algorithm using the front-facing camera of mobile devices to identify human attention hotspots when viewing video type contents. The shortage of computational power in mobile devices becomes a challenge to obtain higher user satisfaction. Edge computing moves the processing power closer to the source of the data and reduces the latency introduced by the cloud computing. Therefore, the proposed algorithm will be extended with mobile edge computing to provide a real-time eye tracking experience for users","PeriodicalId":339393,"journal":{"name":"2022 Symposium on Eye Tracking Research and Applications","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121957088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Lankes, Maurice Sporn, A. Winkelbauer, Barbara Stiglbauer
This paper describes a virtual reality prototype’s game and level design that should serve as stimulus material to arouse confusion among players. In doing so, we will use the stimulus to create a system that analyzes the players’ gaze behavior and determine whether and when the player is confused or already frustrated. Consequently, this information can then be used by game and level designers to give the player appropriate assistance and guidance during gameplay. To reach our goal of creating a design that arouses confusion, we used guidelines for high-quality level design and did the exact opposite. We see the PLEY Workshop as a forum to identify and discuss the potentials and risks of the stimulus’ design and level structure. In general, the paper should provide insights into our design decisions for researchers interested in investigating gaze behavior in games.
{"title":"Looking Confused? – Introducing a VR Game Design for Arousing Confusion Among Players","authors":"M. Lankes, Maurice Sporn, A. Winkelbauer, Barbara Stiglbauer","doi":"10.1145/3517031.3529614","DOIUrl":"https://doi.org/10.1145/3517031.3529614","url":null,"abstract":"This paper describes a virtual reality prototype’s game and level design that should serve as stimulus material to arouse confusion among players. In doing so, we will use the stimulus to create a system that analyzes the players’ gaze behavior and determine whether and when the player is confused or already frustrated. Consequently, this information can then be used by game and level designers to give the player appropriate assistance and guidance during gameplay. To reach our goal of creating a design that arouses confusion, we used guidelines for high-quality level design and did the exact opposite. We see the PLEY Workshop as a forum to identify and discuss the potentials and risks of the stimulus’ design and level structure. In general, the paper should provide insights into our design decisions for researchers interested in investigating gaze behavior in games.","PeriodicalId":339393,"journal":{"name":"2022 Symposium on Eye Tracking Research and Applications","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124413737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Beibin Li, JC Snider, Quan Wang, Sachin Mehta, Claire E. Foster, E. Barney, Linda G. Shapiro, P. Ventola, F. Shic
Gaze calibration is common in traditional infrared oculographic eye tracking. However, it is not well studied in visible-light mobile/remote eye tracking. We developed a lightweight real-time gaze error estimator and analyzed calibration errors from two perspectives: facial feature-based and Monte Carlo-based. Both methods correlated with gaze estimation errors, but the Monte Carlo method associated more strongly. Facial feature associations with gaze error were interpretable, relating movements of the face to the visibility of the eye. We highlight the degradation of gaze estimation quality in a sample of children with autism spectrum disorder (as compared to typical adults), and note that calibration methods may improve Euclidean error by 10%.
{"title":"Calibration Error Prediction: Ensuring High-Quality Mobile Eye-Tracking","authors":"Beibin Li, JC Snider, Quan Wang, Sachin Mehta, Claire E. Foster, E. Barney, Linda G. Shapiro, P. Ventola, F. Shic","doi":"10.1145/3517031.3529634","DOIUrl":"https://doi.org/10.1145/3517031.3529634","url":null,"abstract":"Gaze calibration is common in traditional infrared oculographic eye tracking. However, it is not well studied in visible-light mobile/remote eye tracking. We developed a lightweight real-time gaze error estimator and analyzed calibration errors from two perspectives: facial feature-based and Monte Carlo-based. Both methods correlated with gaze estimation errors, but the Monte Carlo method associated more strongly. Facial feature associations with gaze error were interpretable, relating movements of the face to the visibility of the eye. We highlight the degradation of gaze estimation quality in a sample of children with autism spectrum disorder (as compared to typical adults), and note that calibration methods may improve Euclidean error by 10%.","PeriodicalId":339393,"journal":{"name":"2022 Symposium on Eye Tracking Research and Applications","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128175029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Conventional eye-tracking methods require NIR-LEDs at the corners and edges of displays as references. However, extensive eyeball rotation results in the loss of reflections. Therefore, we propose imperceptible markers that can be dynamically displayed using liquid crystals. Using the characteristics of polarized light, the imperceptible markers are shown on a screen as references for eye-tracking. Additionally, the marker positions can be changed using the eyeball pose in the previous frame. The point-of-gaze was determined using the imperceptible markers based on model-based eye gaze estimation. The accuracy of the estimated PoG obtained using the imperceptible marker was approximately 1.69°, higher than that obtained using NIR-LEDs. Through experiments, we confirmed the feasibility and effectiveness of relocating imperceptible markers on the screen.
{"title":"Gaze Estimation with Imperceptible Marker Displayed Dynamically using Polarization","authors":"Yutaro Inoue, Koki Koshikawa, K. Takemura","doi":"10.1145/3517031.3529640","DOIUrl":"https://doi.org/10.1145/3517031.3529640","url":null,"abstract":"Conventional eye-tracking methods require NIR-LEDs at the corners and edges of displays as references. However, extensive eyeball rotation results in the loss of reflections. Therefore, we propose imperceptible markers that can be dynamically displayed using liquid crystals. Using the characteristics of polarized light, the imperceptible markers are shown on a screen as references for eye-tracking. Additionally, the marker positions can be changed using the eyeball pose in the previous frame. The point-of-gaze was determined using the imperceptible markers based on model-based eye gaze estimation. The accuracy of the estimated PoG obtained using the imperceptible marker was approximately 1.69°, higher than that obtained using NIR-LEDs. Through experiments, we confirmed the feasibility and effectiveness of relocating imperceptible markers on the screen.","PeriodicalId":339393,"journal":{"name":"2022 Symposium on Eye Tracking Research and Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132518579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Teresa Hirzle, Marian Sauter, Tobias Wagner, Susanne Hummel, E. Rukzio, A. Huckauf
Interacting with a group of people requires to direct the attention of the whole group, thus requires feedback about the crowd’s attention. In face-to-face interactions, head and eye movements serve as indicator for crowd attention. However, when interacting online, such indicators are not available. To substitute this information, gaze visualizations were adapted for a crowd scenario. We developed, implemented, and evaluated four types of visualizations of crowd attention in an online study with 72 participants using lecture videos enriched with audience’s gazes. All participants reported increased connectedness to the audience, especially for visualizations depicting the whole distribution of gaze including spatial information. Visualizations avoiding spatial overlay by depicting only the variability were regarded as less helpful, for real-time as well as for retrospective analyses of lectures. Improving our visualizations of crowd attention has the potential for a broad variety of applications, in all kinds of social interaction and communication in groups.
{"title":"Attention of Many Observers Visualized by Eye Movements","authors":"Teresa Hirzle, Marian Sauter, Tobias Wagner, Susanne Hummel, E. Rukzio, A. Huckauf","doi":"10.1145/3517031.3529235","DOIUrl":"https://doi.org/10.1145/3517031.3529235","url":null,"abstract":"Interacting with a group of people requires to direct the attention of the whole group, thus requires feedback about the crowd’s attention. In face-to-face interactions, head and eye movements serve as indicator for crowd attention. However, when interacting online, such indicators are not available. To substitute this information, gaze visualizations were adapted for a crowd scenario. We developed, implemented, and evaluated four types of visualizations of crowd attention in an online study with 72 participants using lecture videos enriched with audience’s gazes. All participants reported increased connectedness to the audience, especially for visualizations depicting the whole distribution of gaze including spatial information. Visualizations avoiding spatial overlay by depicting only the variability were regarded as less helpful, for real-time as well as for retrospective analyses of lectures. Improving our visualizations of crowd attention has the potential for a broad variety of applications, in all kinds of social interaction and communication in groups.","PeriodicalId":339393,"journal":{"name":"2022 Symposium on Eye Tracking Research and Applications","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127058113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Educational systems are based on the teaching inputs from teachers and the learning outputs from the pupils/students. Moreover, also the surrounding environment as well as the social activities and networks of the involved people have an impact on the success of such a system. However, in most of them, standard teaching equipment is used while it is difficult to gain some knowledge and insights about the fine-grained spatio-temporal activities in the classroom. On the other hand, understanding why an educational system is not that successful as it was expected, can hardly be explored by just generating statistics about the learning outputs based on several student-related metrics. In this paper we discuss the benefits of using eye tracking as a powerful technology to track people’s visual attention to explore the value of an educational system, however, we also take a look at the drawbacks that come in the form of data recording, storing, processing, and finally, data analysis and visualization to rapidly gain insights in such large datasets apart from many more. We argue that if eye tracking is applied in a clever way, an educational system might draw valuable conclusions to improve it from several perspectives, be it under the light of online/remote or classroom teaching or also from the perspectives of teachers and pupils/students.
{"title":"The Benefits and Drawbacks of Eye Tracking for Improving Educational Systems","authors":"Michael Burch, Rahel Haymoz, Sabrina Lindau","doi":"10.1145/3517031.3529242","DOIUrl":"https://doi.org/10.1145/3517031.3529242","url":null,"abstract":"Educational systems are based on the teaching inputs from teachers and the learning outputs from the pupils/students. Moreover, also the surrounding environment as well as the social activities and networks of the involved people have an impact on the success of such a system. However, in most of them, standard teaching equipment is used while it is difficult to gain some knowledge and insights about the fine-grained spatio-temporal activities in the classroom. On the other hand, understanding why an educational system is not that successful as it was expected, can hardly be explored by just generating statistics about the learning outputs based on several student-related metrics. In this paper we discuss the benefits of using eye tracking as a powerful technology to track people’s visual attention to explore the value of an educational system, however, we also take a look at the drawbacks that come in the form of data recording, storing, processing, and finally, data analysis and visualization to rapidly gain insights in such large datasets apart from many more. We argue that if eye tracking is applied in a clever way, an educational system might draw valuable conclusions to improve it from several perspectives, be it under the light of online/remote or classroom teaching or also from the perspectives of teachers and pupils/students.","PeriodicalId":339393,"journal":{"name":"2022 Symposium on Eye Tracking Research and Applications","volume":"354 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115930118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Estimating the cognitive load of aircraft pilots is essential to monitor them constantly to identify and overcome unfavorable situations. Presently, the cognitive load of pilots is estimated using manual filling up of forms, and there is a lack of a system that can estimate workload automatically. In this paper, we used eye-tracking technology for cognitive load estimation and developed a Virtual Reality dashboard that visualizes cognitive and ocular data. We undertook a flight simulation study to observe users’ workload during primary and secondary task execution while flying the aircraft. We also undertook an eye-tracking study to identify appropriate 3D graph properties for developing graphs of the cognitive dashboard. We found a significant interaction between users’ primary and secondary tasks. We also observed that primitives like size and color were easier to encode numerical and nominal information. Finally, we developed a dashboard leveraging the results of the 3D graph study for estimating the pilot’s cognitive load. The VR dashboard enabled visualization of the cognitive load parameters derived from the ocular data in real time. The aim of the 3D graph study was to identify the optimal information to be displayed to the participants/pilots. Apart from estimating cognitive load using ocular data, the dashboard can also visualize ocular data collected in a Virtual Reality environment.
{"title":"VR Cognitive Load Dashboard for Flight Simulator","authors":"Somnath Arjun, Archana Hebbar, Sanjana, P. Biswas","doi":"10.1145/3517031.3529777","DOIUrl":"https://doi.org/10.1145/3517031.3529777","url":null,"abstract":"Estimating the cognitive load of aircraft pilots is essential to monitor them constantly to identify and overcome unfavorable situations. Presently, the cognitive load of pilots is estimated using manual filling up of forms, and there is a lack of a system that can estimate workload automatically. In this paper, we used eye-tracking technology for cognitive load estimation and developed a Virtual Reality dashboard that visualizes cognitive and ocular data. We undertook a flight simulation study to observe users’ workload during primary and secondary task execution while flying the aircraft. We also undertook an eye-tracking study to identify appropriate 3D graph properties for developing graphs of the cognitive dashboard. We found a significant interaction between users’ primary and secondary tasks. We also observed that primitives like size and color were easier to encode numerical and nominal information. Finally, we developed a dashboard leveraging the results of the 3D graph study for estimating the pilot’s cognitive load. The VR dashboard enabled visualization of the cognitive load parameters derived from the ocular data in real time. The aim of the 3D graph study was to identify the optimal information to be displayed to the participants/pilots. Apart from estimating cognitive load using ocular data, the dashboard can also visualize ocular data collected in a Virtual Reality environment.","PeriodicalId":339393,"journal":{"name":"2022 Symposium on Eye Tracking Research and Applications","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116178903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Day and night mode is widely used when working with any digital device, including map navigation. Many users have the day and night mode change set automatically. However, it is not proven if this functionality helps improve the transfer of information between the map and the user when changing the lighting conditions. The short paper aims to evaluate the influence of day and night modes on the map users’ perception. User testing was realised in the eye-tracking laboratory with 43 participants. These participants were categorised by the average number of hours spent driving per week and their use of map navigation. The eye-tracking experiment focuses on the orientation of the participants in the day and night mode of map views when changing the lighting conditions. For that, the Euro Truck Simulator game environment was chosen, where the participants were guided by the map navigation in the bottom right corner of the screen. The lighting conditions in the ET laboratory have been adjusted to match the lighting conditions for both day and night as realistically as possible, and the map navigation mode was switched between day and night mode. The explanatory research suggested that using day mode during nighttime may cause disorientation and dazzle; using night mode during daytime does not cause that problem, but the user perception is slightly slower.
{"title":"The Effect of Day and Night Mode on the Perception of Map Navigation Device","authors":"S. Popelka, A. Vondráková, Romana Skulnikova","doi":"10.1145/3517031.3531164","DOIUrl":"https://doi.org/10.1145/3517031.3531164","url":null,"abstract":"Day and night mode is widely used when working with any digital device, including map navigation. Many users have the day and night mode change set automatically. However, it is not proven if this functionality helps improve the transfer of information between the map and the user when changing the lighting conditions. The short paper aims to evaluate the influence of day and night modes on the map users’ perception. User testing was realised in the eye-tracking laboratory with 43 participants. These participants were categorised by the average number of hours spent driving per week and their use of map navigation. The eye-tracking experiment focuses on the orientation of the participants in the day and night mode of map views when changing the lighting conditions. For that, the Euro Truck Simulator game environment was chosen, where the participants were guided by the map navigation in the bottom right corner of the screen. The lighting conditions in the ET laboratory have been adjusted to match the lighting conditions for both day and night as realistically as possible, and the map navigation mode was switched between day and night mode. The explanatory research suggested that using day mode during nighttime may cause disorientation and dazzle; using night mode during daytime does not cause that problem, but the user perception is slightly slower.","PeriodicalId":339393,"journal":{"name":"2022 Symposium on Eye Tracking Research and Applications","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130702239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Riccardo Bovo, D. Giunchi, Ludwig Sidenmark, Hans-Werner Gellersen, E. Costanza, T. Heinis
Eye behavior has gained much interest in the VR research community as an interactive input and support for collaboration. Researchers used head behavior and saliency to implement gaze inference models when eye-tracking is missing. However, these solutions are resource-demanding and thus unfit for untethered devices, and their angle accuracy is around 7°, which can be a problem in high-density informative areas. To address this issue, we propose a lightweight deep learning model that generates the probability density function of the gaze as a percentile contour. This solution allows us to introduce a visual attention representation based on a region rather than a point. In this way, we manage the trade-off between the ambiguity of a region and the error of a point. We tested our model in untethered devices with real-time performances; we evaluated its accuracy, outperforming our identified baselines (average fixation map and head direction).
{"title":"Real-time head-based deep-learning model for gaze probability regions in collaborative VR","authors":"Riccardo Bovo, D. Giunchi, Ludwig Sidenmark, Hans-Werner Gellersen, E. Costanza, T. Heinis","doi":"10.1145/3517031.3529642","DOIUrl":"https://doi.org/10.1145/3517031.3529642","url":null,"abstract":"Eye behavior has gained much interest in the VR research community as an interactive input and support for collaboration. Researchers used head behavior and saliency to implement gaze inference models when eye-tracking is missing. However, these solutions are resource-demanding and thus unfit for untethered devices, and their angle accuracy is around 7°, which can be a problem in high-density informative areas. To address this issue, we propose a lightweight deep learning model that generates the probability density function of the gaze as a percentile contour. This solution allows us to introduce a visual attention representation based on a region rather than a point. In this way, we manage the trade-off between the ambiguity of a region and the error of a point. We tested our model in untethered devices with real-time performances; we evaluated its accuracy, outperforming our identified baselines (average fixation map and head direction).","PeriodicalId":339393,"journal":{"name":"2022 Symposium on Eye Tracking Research and Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130808715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Calibration is performed in eye-tracking studies to map raw model outputs to gaze-points on the screen and improve accuracy of gaze predictions. Calibration parameters, such as user-screen distance, camera intrinsic properties, and position of the screen with respect to the camera can be easily calculated in controlled offline setups, however, their estimation is non-trivial in unrestricted, online, experimental settings. Here, we propose the application of deep learning models for eye-tracking in online experiments, providing suitable strategies to estimate calibration parameters and perform personal gaze calibration. Focusing on fixation accuracy, we compare results with respect to calibration frequency, the time point of calibration during data collection (beginning, middle, end), and calibration procedure (fixation-point or smooth pursuit-based). Calibration using fixation and smooth pursuit tasks, pooled over three collection time-points, resulted in the best fixation accuracy. By combining device calibration, gaze calibration, and the best-performing deep-learning model, we achieve an accuracy of 2.580−a considerable improvement over reported accuracies in previous online eye-tracking studies.
{"title":"Towards efficient calibration for webcam eye-tracking in online experiments","authors":"Shreshtha Saxena, Elke B. Lange, Lauren Fink","doi":"10.1145/3517031.3529645","DOIUrl":"https://doi.org/10.1145/3517031.3529645","url":null,"abstract":"Calibration is performed in eye-tracking studies to map raw model outputs to gaze-points on the screen and improve accuracy of gaze predictions. Calibration parameters, such as user-screen distance, camera intrinsic properties, and position of the screen with respect to the camera can be easily calculated in controlled offline setups, however, their estimation is non-trivial in unrestricted, online, experimental settings. Here, we propose the application of deep learning models for eye-tracking in online experiments, providing suitable strategies to estimate calibration parameters and perform personal gaze calibration. Focusing on fixation accuracy, we compare results with respect to calibration frequency, the time point of calibration during data collection (beginning, middle, end), and calibration procedure (fixation-point or smooth pursuit-based). Calibration using fixation and smooth pursuit tasks, pooled over three collection time-points, resulted in the best fixation accuracy. By combining device calibration, gaze calibration, and the best-performing deep-learning model, we achieve an accuracy of 2.580−a considerable improvement over reported accuracies in previous online eye-tracking studies.","PeriodicalId":339393,"journal":{"name":"2022 Symposium on Eye Tracking Research and Applications","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116746387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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