Tri-Cam: Practical Eye Gaze Tracking via Camera Network
This provides a more practical and affordable gaze tracking solution for human-computer interaction and research applications, though it is incremental in improving existing methods.
The paper tackled the problem of gaze tracking systems being limited by user movement and calibration effort by introducing Tri-Cam, a system using three affordable webcams with an implicit calibration module, achieving comparable accuracy to a commercial eye tracker while supporting a wider free movement area.
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.