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E-Gaze: Gaze Estimation With Event Camera.

Nealson Li, Muya Chang, Arijit Raychowdhury

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |January 29, 2024
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel real-time gaze estimation system using event cameras, achieving high accuracy for fast eye movements. It

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    Area of Science:

    • Computer Vision
    • Biomedical Engineering
    • Neuroscience

    Background:

    • Near-eye gaze estimation traditionally uses frame-based cameras.
    • Event cameras offer high speed and dynamic range, ideal for rapid eye movements.
    • Existing methods are incompatible with event-based data due to differing characteristics.

    Purpose of the Study:

    • To develop a real-time gaze estimation system utilizing only event-based camera data.
    • To analyze near-eye event data patterns and extract relevant eye features.
    • To adapt algorithms for the unique properties of event camera streams.

    Main Methods:

    • Developed a real-time pipeline to extract pupil features by analyzing event data distribution (polar, spatial, temporal).
    • Utilized a recurrent neural network with a novel coordinate-to-angle loss function for gaze prediction.
    • Processed asynchronous, sparse data streams from event cameras.

    Main Results:

    • Achieved high-accuracy real-time gaze estimation with an angular accuracy of 0.46 degrees.
    • Demonstrated system update rates of 950 Hz, suitable for tracking fast eye movements.
    • Successfully processed event-based data for gaze estimation, a novel approach.

    Conclusions:

    • The developed system enables accurate, high-speed gaze estimation using only event camera data.
    • This work pioneers gaze estimation solely on event-based streams, opening new application possibilities.
    • The findings highlight the potential of event cameras in human-computer interaction and eye-tracking research.