Gaze depth estimation for eye-tracking systems

Abigail Stone; Srijith Rajeev; Shishir Paramathma Rao; Karen Panetta; Sos Agaian; Aaron Gardony; Jessica Nordlund; Rebecca Skantar

doi:10.1117/12.2664140

15 June 2023 Gaze depth estimation for eye-tracking systems

Abigail Stone, Srijith Rajeev, Shishir Paramathma Rao, Karen Panetta, Sos Agaian, Aaron Gardony, Jessica Nordlund, Rebecca Skantar

Author Affiliations +

Proceedings Volume 12526, Multimodal Image Exploitation and Learning 2023 ; 125260N (2023) https://doi.org/10.1117/12.2664140
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Conference Poster

Abstract

Eye-tracking holds numerous promises for improving the mixed reality experience. While eye-tracking devices are capable of accurate gaze mapping on 2D surfaces, depth estimation of gaze points remains a challenging problem. Most gaze-based interaction applications are supported by estimation techniques that find a mapping between gaze data and corresponding targets on a 2D surface. This approach inevitably leads to a biased outcome, as the nearest objects in the line of sight will tend to be the target of interest more often. One viable solution would be to estimate gaze as a 3D coordinate (x, y, z) rather than the traditional 2D coordinate (x, y). This article first introduces a new comprehensive 3D gaze dataset collected in a realistic scene setting. Data was collected using a head-mounted eye-tracker and a depth estimation camera. Next, we present a novel depth estimation model, trained on the new gaze dataset to accurately predict gaze depth based on calibrated gaze vectors. This method could help develop a mapping between gaze and 3D objects on a 3D plane. The presented model improves the reliability of depth measurement of visual attention in real scenes as well as the accuracy of depth-based scenes in virtual reality environments. Improving situational awareness using 3D gaze data will benefit several domains, particularly human-vehicle interaction, autonomous driving, and augmented reality.

Citation Download Citation

Abigail Stone, Srijith Rajeev, Shishir Paramathma Rao, Karen Panetta, Sos Agaian, Aaron Gardony, Jessica Nordlund, and Rebecca Skantar "Gaze depth estimation for eye-tracking systems", Proc. SPIE 12526, Multimodal Image Exploitation and Learning 2023 , 125260N (15 June 2023); https://doi.org/10.1117/12.2664140

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