论文标题
流星和流星样现象的视频数据上的对象分类：算法和数据
Object classification on video data of meteors and meteor-like phenomena: algorithm and data
论文作者
论文摘要
每时每刻，无数的流星体进入我们的气氛。对流星的检测和测量提供了独特的机会，可以洞悉太阳系天体的组成。因此，研究人员进行了一个广阔的天空监视器，以保护360度视频材料，从而节省了流星的每个条目。现有的机器智能无法准确识别流星事件，因为缺乏公开可用的高质量培训数据，因此与地球大气相交的流星事件。由于缺乏可用的标签高质量培训数据，这项工作为接受我们收集的数据培训的研究人员提供了四个可重复使用的开源解决方案。我们将提出的数据集称为夜间数据集，由10,000个流星和10,000个非数字事件组成。我们的解决方案采用各种机器学习技术，即分类，特征学习，异常检测和推断。对于分类任务，达到99.1 \％的平均准确性。代码和数据在figshare上公开使用：10.6084/m9.figshare.16451625
Every moment, countless meteoroids enter our atmosphere unseen. The detection and measurement of meteors offer the unique opportunity to gain insights into the composition of our solar systems' celestial bodies. Researchers, therefore, carry out a wide-area-sky-monitoring to secure 360-degree video material, saving every single entry of a meteor. Existing machine intelligence cannot accurately recognize events of meteors intersecting the earth's atmosphere due to a lack of high-quality training data publicly available. This work presents four reusable open source solutions for researchers trained on data we collected due to the lack of available labeled high-quality training data. We refer to the proposed dataset as the NightSkyUCP dataset, consisting of a balanced set of 10,000 meteor- and 10,000 non-meteor-events. Our solutions apply various machine learning techniques, namely classification, feature learning, anomaly detection, and extrapolation. For the classification task, a mean accuracy of 99.1\% is achieved. The code and data are made public at figshare with DOI: 10.6084/m9.figshare.16451625
