Exploring Temporal Dynamics in Event-based Eye Tracker
Journal:
arXiv
Published Date:
Mar 31, 2025
Abstract
Eye-tracking is a vital technology for human-computer interaction, especially
in wearable devices such as AR, VR, and XR. The realization of high-speed and
high-precision eye-tracking using frame-based image sensors is constrained by
their limited temporal resolution, which impairs the accurate capture of rapid
ocular dynamics, such as saccades and blinks. Event cameras, inspired by
biological vision systems, are capable of perceiving eye movements with
extremely low power consumption and ultra-high temporal resolution. This makes
them a promising solution for achieving high-speed, high-precision tracking
with rich temporal dynamics. In this paper, we propose TDTracker, an effective
eye-tracking framework that captures rapid eye movements by thoroughly modeling
temporal dynamics from both implicit and explicit perspectives. TDTracker
utilizes 3D convolutional neural networks to capture implicit short-term
temporal dynamics and employs a cascaded structure consisting of a
Frequency-aware Module, GRU, and Mamba to extract explicit long-term temporal
dynamics. Ultimately, a prediction heatmap is used for eye coordinate
regression. Experimental results demonstrate that TDTracker achieves
state-of-the-art (SOTA) performance on the synthetic SEET dataset and secured
Third place in the CVPR event-based eye-tracking challenge 2025. Our code is
available at https://github.com/rhwxmx/TDTracker.
