[visionlist] [CfP] ActiveEye Workshop - Challenges in large scale eye-tracking for active participants
agostino.gibaldi at berkeley.edu
Fri Feb 5 04:58:40 -04 2021
Call for papers
ActivEye Workshop 2021
Challenges in large scale eye tracking for active participants
We invite you to submit your work to the ActivEye Workshop 2021. As part of
this year’s ACM ETRA 2021 conference (Eye Tracking Research and
Application), we’re inviting two-page submissions in one of the areas below
in order to bring together the vision, engineering, human factors, and
computer science communities to share our solutions that address the
following challenges in eye tracking:
Robust Gaze tracking in a challenging environment:
Topics include algorithms for 2D and 3D gaze tracking of active
participants outside of the lab, slippage detection and correction,
run-time and post-hoc re-calibration, and validation techniques.
Head pose tracking:
We invite studies that take into account the synergy between the eye and
head movements, especially encourage contributions to onboard head pose
tracking systems, post-hoc head tracking techniques, and head + eye
movement classification algorithms in order to better understand the
underlying oculomotor mechanisms.
World Camera Characteristics:
Typically, portable eye-tracking devices have a limited field of view, poor
optics, and low-quality world video. We encourage studies that address
issues of image quality, dynamic range, wide FOV gaze tracking, color
consistency, frame rate, spatial resolution, quality vs. size compromise,
and modern depth-sensing techniques.
Extending use cases to special populations:
The day-to-day challenges of mobile eye-tracking are often exacerbated
under certain experimental conditions and with specific participant
populations. The latter can include children and older adults, who might
have additional ergonomic and physiological constraints; or patient
populations (e.g., those with strabismus or visual field damage).
Gaze Tracking Data Annotation:
Considering the growing demands for state of the art deep learning
techniques, a key feature of a usable dataset is that it is accurately and
reliably annotated. We welcome submissions that include efforts for
annotating different aspects of such datasets efficiently, including but
not limited to different types of eye + head movements, external events,
different eye regions for real or synthetic images and scene objects.
Best Practices and DIY:
We encourage submissions on best practices that researchers take into
account when running large scale data collection, such as comparisons of
different calibration routines, efforts to enhance participant comfort,
experimental procedures, UI design for system error tolerance, error
handling, and notifications.
Participant discomfort with most head-mounted trackers is a key obstacle in
the collection of large-scale and outdoor data, particularly for extended
time periods. We welcome submissions where researchers in academia and
industry share their experience and potential solutions.
ETRA 2021 website:
Workshop email address:
activeye_etra2021 at googlegroups.com
March 8, 2021
Kamran Binaee (University of Nevada, Reno) (kbinaee at unr.edu)
Natela Shanidze (The Smith-Kettlewell Eye Research Institute) (
natela at ski.org)
Agostino Gibaldi (University of California, Berkeley) (
agostino.gibaldi at berkeley.edu)
Caroline Robertson (Dartmouth College) (caroline.e.robertson at dartmouth.edu)
Paul Macneilage (University of Nevada, Reno) (pmacneilage at unr.edu)
Mark Lescroart (University of Nevada, Reno) (mlescroart at unr.edu)
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