Advances in the meteor image processing chain using fast algorithms, deep learning, and empirical fitting

Abstract

The processing pipeline for both video meteor detection and track analysis has evolved to embrace several new algorithms, which have improved the efficiency and performance of various steps in the meteor image processing chain. With the advent of larger pixel count digital sensors, the image processing techniques have needed to keep up with the computational load by not only employing higher end processors, but developing faster thresholding, clustering, and tracking algorithms for detection. In addition, machine learning methods employing both recurrent and convolutional deep neural networks have helped remove the human-in-loop false alarm mitigation step inherent in many meteor collection processing streams. The application of a matched filtering algorithm has helped to refine the measurement positional accuracy of propagating meteor tracks for post-detection analysis. The use of improved multi-site track aggregation has dramatically reduced the occurrence of mis-associating unrelated tracks during the combination into a single trajectory. When coupled with an improved minimization metric in the multi-parameter fitting method for trajectory estimation, this yields better meteor orbital solutions. Finally, proposed concepts in using a convolutional neural network as a meteor detector and performing trajectory fitting with an empirically based propagation model, show promise for more robust meteor image processing and analysis in the near future.