Related papers: Realtime Dynamic Gaze Target Tracking and Depth-Level Estimation

Realtime Dynamic Gaze Target Tracking and Depth-Level Estimation

URL: http://arxiv.org/abs/2406.18595v1
Date: Sun, 9 Jun 2024 20:52:47 GMT
Title: Realtime Dynamic Gaze Target Tracking and Depth-Level Estimation
Authors: Esmaeil Seraj, Harsh Bhate, Walter Talamonti,
Abstract summary: Transparent Displays (TD) in various applications, such as Heads-Up Displays (HUDs) in vehicles, is a burgeoning field, poised to revolutionize user experiences. 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. 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; and (2) a multi-stream self-attention architecture to estimate the depth-level of human gaze from eye tracking data.
Score: 6.435984242701043
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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.