Tri-Cam: Practical Eye Gaze Tracking via Camera Network
Sikai Yang, Wan Du

TL;DR
Tri-Cam is a practical, deep learning-based gaze tracking system using three webcams that offers accurate, calibration-free tracking during free movement, suitable for diverse human-computer interaction applications.
Contribution
Introduces Tri-Cam, a novel multi-camera gaze tracking system with implicit calibration, achieving comparable accuracy to commercial trackers while supporting free user movement.
Findings
Achieves similar accuracy to Tobii eye tracker
Supports wider free movement area
Reduces calibration effort through implicit calibration
Abstract
As human eyes serve as conduits of rich information, unveiling emotions, intentions, and even aspects of an individual's health and overall well-being, gaze tracking also enables various human-computer interaction applications, as well as insights in psychological and medical research. However, existing gaze tracking solutions fall short at handling free user movement, and also require laborious user effort in system calibration. We introduce Tri-Cam, a practical deep learning-based gaze tracking system using three affordable RGB webcams. It features a split network structure for efficient training, as well as designated network designs to handle the separated gaze tracking tasks. Tri-Cam is also equipped with an implicit calibration module, which makes use of mouse click opportunities to reduce calibration overhead on the user's end. We evaluate Tri-Cam against Tobii, the…
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Taxonomy
TopicsGaze Tracking and Assistive Technology · Video Surveillance and Tracking Methods · Advanced Computing and Algorithms
