  The integration of Transparent Displays (TD) in various applications, such as
Heads-Up Displays (HUDs) in vehicles, is a burgeoning field, poised to
revolutionize user experiences. However, this innovation brings forth
significant challenges in realtime human-device interaction, particularly in
accurately identifying and tracking a user's gaze on dynamically changing TDs.
In this paper, we present a two-fold robust and efficient systematic solution
for realtime gaze monitoring, comprised of: (1) a tree-based algorithm for
identifying and dynamically tracking gaze targets (i.e., moving, size-changing,
and overlapping 2D content) projected on a transparent display, in realtime;
(2) a multi-stream self-attention architecture to estimate the depth-level of
human gaze from eye tracking data, to account for the display's transparency
and preventing undesired interactions with the TD. We collected a real-world
eye-tracking dataset to train and test our gaze monitoring system. We present
extensive results and ablation studies, including inference experiments on
System on Chip (SoC) evaluation boards, demonstrating our model's scalability,
precision, and realtime feasibility in both static and dynamic contexts. Our
solution marks a significant stride in enhancing next-generation user-device
interaction and experience, setting a new benchmark for algorithmic gaze
monitoring technology in dynamic transparent displays.
