Using Priors to Compensate Geometrical Problems in Head-Mounted Eye Trackers

Aug 24, 2017·
Fabricio Batista Narcizo
Fabricio Batista Narcizo
,
Zaheer Ahmed
,
Dan Witzner Hansen
· 0 min read
Abstract
The use of additional information (a.k.a. priors) to help the eye tracking process is presented as an alternative to compensate classical geometrical problems in head-mounted eye trackers. Priors can be obtained from several distinct sources, such as – sensors to collect information related to distance, location, luminance, movement, speed; information extracted directly from the scene camera; calibration of video capture devices and other components of the eye tracker; information collected from a totally controlled environment; among others. Thus, priors are used to improve the robustness of eye tracking in real applications, for example, (1) If the distance between the subject and the viewed target is known, it is possible to estimate subject{\textquoteright}s current point of regard even when target moves in depth and suffers influence of parallax error; and (2) if the tridimensional angular rotation is known, it is possible to compensate the error induced by the head rotations using linear regression. Experiments with simulated eye tracking data and in real scenarios of elite sports have been showing that the use of priors to support the eye tracking systems help produce more accurate and precise gaze estimation specially for uncalibrated head-mounted setups.
Type
Publication
In 19th European Conference on Eye Movements
publications
Fabricio Batista Narcizo
Authors
Senior AI Research Scientist
Fabricio Batista Narcizo is a Senior AI Research Scientist in the Video Technology department at GN Hearing A/S (Jabra) and a Part-Time Lecturer and Course Manager at the IT University of Copenhagen (ITU). He received his Ph.D. degree in Computer Science from the ITU in 2017, his M.Sc. degree in Electronic & Computer Engineering from the Aeronautics Institute of Technology (ITA) in 2008, and his B.Sc. degree in Computer Science from the University of Western Santa Catarina (UNOESC) in 2005. His research interests lie in computer vision, image analysis, artificial intelligence, data science, data mining, machine learning, edge AI, and human-computer interaction, with a particular interest in eye-tracking.