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

Anastasios N. Angelopoulos, Julien N.P. Martel, Amit P. Kohli, Jorg Conradt, Gordon Wetzstein

Research output: Contribution to journalArticlepeer-review

30 Scopus citations


Fast and accurate eye tracking is crucial for many applications. Current camera-based eye tracking systems, however, are fundamentally limited by their bandwidth, forcing a tradeoff between image resolution and framerate, i.e. between latency and update rate. 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 eye trackers when evaluated in the same conditions. Our system 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 gaze vector from the parametric pupil model in real time. Using the first hybrid frame-event gaze dataset, which will be made public, we demonstrate that our system achieves accuracies of 0.45 degrees -- 1.75 degrees for fields of view ranging from 45 degrees to 98 degrees.
Original languageEnglish (US)
Pages (from-to)2577-2586
Number of pages10
JournalIEEE Transactions on Visualization and Computer Graphics
Issue number5
StatePublished - May 2021
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2021-06-24
Acknowledgements: A.N.A. was supported by a National Science Foundation (NSF) Fellowship and a Berkeley Fellowship. J.N.P.M. was supported by a Swiss
National Foundation (SNF) Fellowship (P2EZP2 181817), G.W. was supported by an NSF CAREER Award (IIS 1553333), a Sloan Fellowship, by the KAUST Office of Sponsored Research through the Visual Computing Center CCF grant, and a PECASE by the ARL. Thanks to Stephen Boyd and Mert Pilanci for helpful conversations.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.

ASJC Scopus subject areas

  • Signal Processing
  • Computer Graphics and Computer-Aided Design
  • Software
  • Computer Vision and Pattern Recognition


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