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arXiv · 2208.10581

EBSnoR: Event-Based Snow Removal by Optimal Dwell Time Thresholding

Abigail Wolf·Shannon Brooks-Lehnert·Keigo Hirakawa
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Abstract

We propose an Event-Based Snow Removal algorithm called EBSnoR. We developed a technique to measure the dwell time of snowflakes on a pixel using event-based camera data, which is used to carry out a Neyman-Pearson hypothesis test to partition event stream into snowflake and background events. The effectiveness of the proposed EBSnoR was verified on a new dataset called UDayton22EBSnow, comprised of front-facing event-based camera in a car driving through snow with manually annotated bounding boxes around surrounding vehicles. Qualitatively, EBSnoR correctly identifies events corresponding to snowflakes; and quantitatively, EBSnoR-preprocessed event data improved the performance of event-based car detection algorithms.

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Abigail Wolf, Shannon Brooks-Lehnert, Keigo Hirakawa. 2022-08-22. EBSnoR: Event-Based Snow Removal by Optimal Dwell Time Thresholding. https://arxiv.org/abs/2208.10581

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