Meteor cluster event indication in variable-length astronomical video sequences

Bednář, Jan; Krauz, Lukáš; Páta, Petr; Koten, Pavel

doi:10.1093/mnras/stad1578

Cited by 1 publication

(1 citation statement)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A subset of randomly selected points from the image is considered as sample points in the PHT, which then finds other connected sample points and accumulates votes based on their relationships to determine the parameters of the lines. The linear features and positions of the moving object can be extracted by utilizing the computation and analysis of the PHT [27].…”

Section: Detection and Clustering Of Moving Objectsmentioning

confidence: 99%

A Meteor Detection Algorithm for GWAC System

Chen,

Li,

Liu

et al. 2023

Universe

View full text Add to dashboard Cite

Compared with the international meteor surveillance systems, the ground wide angle camera (GWAC) system exhibits characteristics such as images with the resolution of 4K × 4K and single-site observation. These characteristics present challenges for meteor detection in the GWAC system. Consequently, this paper proposes a new meteor detection algorithm for the GWAC system on the base of the solely mini-GWAC system data algorithm. The new algorithm consists of the following key steps: (1) to compare differences between adjacent frames, applying block-based image binarization thresholds, and incorporating median filtering to reduce noise; (2) to adopt the probabilistic Hough transform (PHT) to identify moving objects and cluster them based on the origin moment of the line segments, while assessing the credibility of clustering; (3) to introduce the so-called maximum disappearance frame for moving objects in the tracking algorithm, enhancing the ability to track multi-frame moving objects. The utilization of the line segment inclination angle of the moving object as the direction of movement facilitates the tracking of multiple moving objects, thereby reducing the probability of mistakenly selecting single-frame moving objects; (4) to leverage the light curves of single-frame moving objects to select meteors to enhance the accuracy of meteor detection. Comparative experiments demonstrate that our proposed algorithm processes each frame image in just 0.39 s, achieving an accuracy of 89.8% in the dataset of 5856 adjacent frames. The experimental results indicate that the algorithm achieved an accuracy of 90.27% when applied in the meteor detection of the image data captured by the GWAC system from Dec. 10th to 19th in 2019 and 2021, obtaining excellent detection results.

show abstract

Section: Detection and Clustering Of Moving Objectsmentioning

confidence: 99%