Event-Based Near-Eye Gaze Tracking Beyond 10,000 Hz

The cameras in modern gaze-tracking systems suffer from fundamental bandwidth and power limitations, constraining data acquisition speed to 300 Hz realistically. This obstructs the use of mobile eye trackers to perform, e.g., low latency predictive rendering, or to study quick and subtle eye motions...

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Vydáno v:IEEE transactions on visualization and computer graphics Ročník 27; číslo 5; s. 2577 - 2586
Hlavní autoři: Angelopoulos, Anastasios N., Martel, Julien N.P., Kohli, Amit P., Conradt, Jorg, Wetzstein, Gordon
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.05.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Augmented and virtual reality
Augmented reality
Bandwidth
Cameras
Event-based camera
Eye movements
Eye tracking
Gaze tracking
Polynomials
Pupils
Real-time systems
Rendering
Rendering (computer graphics)
Tracking
Tracking systems
Two dimensional models
Virtual reality
ISSN:1077-2626, 1941-0506, 1941-0506
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Popis
Shrnutí:The cameras in modern gaze-tracking systems suffer from fundamental bandwidth and power limitations, constraining data acquisition speed to 300 Hz realistically. This obstructs the use of mobile eye trackers to perform, e.g., low latency predictive rendering, or to study quick and subtle eye motions like microsaccades using head-mounted devices in the wild. Here, we propose a hybrid frame-event-based near-eye gaze tracking system offering update rates beyond 10,000 Hz with an accuracy that matches that of high-end desktop-mounted commercial trackers when evaluated in the same conditions. Our system, previewed in Figure 1, builds on emerging event cameras that simultaneously acquire regularly sampled frames and adaptively sampled events. We develop an online 2D pupil fitting method that updates a parametric model every one or few events. Moreover, we propose a polynomial regressor for estimating the point of gaze from the parametric pupil model in real time. Using the first event-based gaze dataset, we demonstrate that our system achieves accuracies of 0.45°-1.75° for fields of view from 45° to 98°. With this technology, we hope to enable a new generation of ultra-low-latency gaze-contingent rendering and display techniques for virtual and augmented reality.
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ISSN:1077-2626
1941-0506
1941-0506
DOI:10.1109/TVCG.2021.3067784