Efficient Eye Tracking and Gaze Estimation with Near-eye Event Cameras

Abstract

Eye tracking is a key enabling technology for wearable and extended-reality devices, but conventional frame-based systems struggle to capture the eye's rapid motion within the tight compute and power budgets of such hardware. Event cameras, which report per-pixel brightness changes asynchronously at microsecond resolution, are a natural fit for this setting, yet most event-based methods rely on heavy neural networks that are impractical to deploy on resource-constrained devices. This thesis presents a lightweight, training-free pipeline for near-eye gaze estimation that maps the tracked pupil to a point of gaze on the screen. The pupil is first detected in gray-scale frames using a purely geometric procedure of thresholding, morphological filtering, and ellipse fitting, and its center is then propagated between frames directly on the event stream by a points-to-edge template tracker, providing high-frequency updates without reconstructing an image. The pupil observation is mapped to gaze using a polynomial regressor. Evaluated on two near-eye datasets, the geometric detector matches a model-based baseline and approaches a supervised segmentation network, while the full system runs end-to-end in under a millisecond on a CPU - several orders of magnitude cheaper than learned alternatives. The work trades some gaze accuracy for the ability to run without a GPU or training data, at the cost of a small number of per-subject detection thresholds, offering a practical path toward efficient, deployable event-based eye tracking.