论文标题
EBSNOR：通过最佳停留时间阈值取消事件的降雪
EBSnoR: Event-Based Snow Removal by Optimal Dwell Time Thresholding
论文作者
论文摘要
我们提出了一种基于事件的降雪算法，称为EBSNOR。我们开发了一种技术，可以使用基于事件的相机数据来测量像素上雪花的停留时间，该数据用于进行Neyman-Pearson假设测试，以将事件流分为雪花和背景事件。在一个名为udayton22ebsnow的新数据集上验证了拟议的eBSNOR的有效性，该数据集由前面事件的摄像机组成，在汽车中驾驶雪中，手动注释周围的车辆周围的界限。定性地，Ebsnor正确识别了与雪花相对应的事件；并且在数量上，EBSNOR备案的事件数据改善了基于事件的CAR检测算法的性能。
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.
