GazeProjector

Lander, Christian; Gehring, Sven; Krüger, Antonio; Boring, Sebastian; Bulling, Andreas

doi:10.1145/2807442.2807479

Cited by 34 publications

(12 citation statements)

References 31 publications

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“…This means that the template of the surface -here the displayis detected more accurately, and thus the computation of the homography matrix is more robust. The results for MT are in line with existing findings [11]. Calibrating from the far location leads to a smaller calibration plane than for near locations.…”

Section: Gaze Estimationsupporting

confidence: 90%

“…Our first approach is similar to GazeProjector [11] and Gaze+RST [41]. Instead of utilizing the world camera of (2) removing glue around the lens to adjust focus; (3) camera is built into a custom enclosure -rotatable and movable -and (4) mounted on a glasses frame, connected to a (5) RaspberryPi.…”

Section: Gaze Estimation On Displaysmentioning

confidence: 99%

“…We compared our two approaches with corresponding versions using distortion-corrected corneal images, a head-mounted Pupil Labs eye tracker that uses marker tracking to estimate its relative position to the display [11], and an approach with a Kinect v2 sensor that uses only the head position and orientation.…”

Section: Experiments -Gaze Estimation On a Displaymentioning

confidence: 99%

“…The first uses the k-means cluster of all extracted key feature pairs as the gaze point on the surface. The second roughly extracts the pupil center and maps it to the display using a homography matrix, based on the extracted key feature pairs [11,41]. Both of our methods require knowledge about the surface's content.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Eyemirror

Lander

Gehring

Löchtefeld

et al. 2017

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

Self Cite

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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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Section: Gaze Estimationsupporting

confidence: 90%

Section: Gaze Estimation On Displaysmentioning

confidence: 99%

Section: Experiments -Gaze Estimation On a Displaymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Eyemirror

Lander

Gehring

Löchtefeld

et al. 2017

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

Self Cite

View full text Add to dashboard Cite

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“…includes: [64,91,92,110,124,183,223,226,276,295,339,353] Figure 1: Ontology of cross-device research terminology.…”

Section: Methodsmentioning

confidence: 99%

Cross-Device Taxonomy

Brudy

Holz

Rädle

et al. 2019

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

185

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Designing interfaces or applications that move beyond the bounds of a single device screen enables new ways to engage with digital content. Research addressing the opportunities and challenges of interactions with multiple devices in concert is of continued focus in HCI research. To inform the future research agenda of this field, we contribute an analysis and taxonomy of a corpus of 510 papers in the crossdevice computing domain. For both new and experienced researchers in the field we provide: an overview, historic trends and unified terminology of cross-device research; discussion of major and under-explored application areas; mapping of enabling technologies; synthesis of key interaction techniques spanning across multiple devices; and review of common evaluation strategies. We close with a discussion of open issues. Our taxonomy aims to create a unified terminology and common understanding for researchers in order to facilitate and stimulate future cross-device research.

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Esquisse: Using 3D Models Staging to Facilitate the Creation of Vector-Based Trace Figures

Antoine

Malacria

Marquardt

et al. 2019

Human-Computer Interaction – INTERACT 2019

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Trace figures are contour drawings of people and objects that capture the essence of scenes without the visual noise of photos or other visual representations. Their focus and clarity make them ideal representations to illustrate designs or interaction techniques. In practice, creating those figures is a tedious task requiring advanced skills, even when creating the figures by tracing outlines based on photos. To mediate the process of creating trace figures, we introduce the open-source tool Esquisse. Informed by our taxonomy of 124 trace figures, Esquisse provides an innovative 3D model staging workflow, with specific interaction techniques that facilitate 3D staging through kinematic manipulation, anchor points and posture tracking. Our rendering algorithm (including stroboscopic rendering effects) creates vector-based trace figures of 3D scenes. We validated Esquisse with an experiment where participants created trace figures illustrating interaction techniques, and results show that participants quickly managed to use and appropriate the tool.

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GazeProjector

Cited by 34 publications

References 31 publications

Eyemirror

Eyemirror

Cross-Device Taxonomy

Esquisse: Using 3D Models Staging to Facilitate the Creation of Vector-Based Trace Figures

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