Tri-Cam: Practical Eye Gaze Tracking via Camera Network

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 state-of-the-art commercial eye tracker, achieving comparable accuracy, while supporting a wider free movement area. In conclusion, Tri-Cam provides a user-friendly, affordable, and robust gaze tracking solution that could practically enable various applications.