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.
Cite
@article{arxiv.2208.10581,
title = {EBSnoR: Event-Based Snow Removal by Optimal Dwell Time Thresholding},
author = {Abigail Wolf and Shannon Brooks-Lehnert and Keigo Hirakawa},
journal= {arXiv preprint arXiv:2208.10581},
year = {2022}
}