EBSnoR: Event-Based Snow Removal by Optimal Dwell Time Thresholding
This addresses snow removal for event-based cameras in autonomous driving, but it is incremental as it applies existing statistical methods to a new domain-specific dataset.
The paper tackled the problem of removing snowflake noise from event-based camera data by developing EBSnoR, which uses dwell time measurements and a hypothesis test to partition events, resulting in improved performance for car detection algorithms on a new dataset.
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.