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"Seeing" ENF From Neuromorphic Events: Modeling and Robust Estimation.

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    This study introduces a novel method for estimating Electric Network Frequency (ENF) using event cameras, overcoming limitations of traditional video-based approaches. Event-based ENF (E-ENF) offers superior accuracy, especially in challenging environments.

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    Area of Science:

    • Computer Vision
    • Signal Processing
    • Neuromorphic Engineering

    Background:

    • Artificial lights fluctuate with grid alternating current, enabling Electric Network Frequency (ENF) estimation from videos.
    • Video-based ENF (V-ENF) estimation is limited by imaging quality, non-ideal sampling, and challenging environmental conditions.

    Purpose of the Study:

    • To develop a robust method for ENF estimation using event cameras, overcoming the limitations of V-ENF.
    • To validate the physical mechanism of ENF capture in event camera data.
    • To introduce a new dataset for evaluating event-based ENF estimation.

    Main Methods:

    • Formulated and validated the physical mechanism for ENF capture in event camera data.
    • Proposed an Event-based ENF (E-ENF) estimation method utilizing mode filtering and harmonic enhancement.
    • Created the Event-Video ENF Dataset (EV-ENFD) and its extension (EV-ENFD+) with diverse scenarios.

    Main Results:

    • Demonstrated that ENF can be extracted from event camera data without the limitations of V-ENF.
    • The proposed E-ENF method significantly outperforms V-ENF in accuracy.
    • E-ENF shows superior performance in challenging environments, including static, dynamic, and extreme lighting scenes.

    Conclusions:

    • Event cameras offer a promising new modality for robust ENF estimation.
    • The developed E-ENF method provides a significant advancement over existing V-ENF techniques.
    • The EV-ENFD datasets are valuable resources for future research in event-based ENF analysis.