Space Object Detection using Multi-frame Temporal Trajectory Completion Method
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866917176596758528 |
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| author | Lan, Xiaoqing Xin, Biqiao Wang, Bingshu Zhang, Han Zhu, Rui Zhang, Laixian |
| author_facet | Lan, Xiaoqing Xin, Biqiao Wang, Bingshu Zhang, Han Zhu, Rui Zhang, Laixian |
| contents | Space objects in Geostationary Earth Orbit (GEO) present significant detection challenges in optical imaging due to weak signals, complex stellar backgrounds, and environmental interference. In this paper, we enhance high-frequency features of GEO targets while suppressing background noise at the single-frame level through wavelet transform. Building on this, we propose a multi-frame temporal trajectory completion scheme centered on the Hungarian algorithm for globally optimal cross-frame matching. To effectively mitigate missing and false detections, a series of key steps including temporal matching and interpolation completion, temporal-consistency-based noise filtering, and progressive trajectory refinement are designed in the post-processing pipeline. Experimental results on the public SpotGEO dataset demonstrate the effectiveness of the proposed method, achieving an F_1 score of 90.14%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_19220 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Space Object Detection using Multi-frame Temporal Trajectory Completion Method Lan, Xiaoqing Xin, Biqiao Wang, Bingshu Zhang, Han Zhu, Rui Zhang, Laixian Computer Vision and Pattern Recognition Space objects in Geostationary Earth Orbit (GEO) present significant detection challenges in optical imaging due to weak signals, complex stellar backgrounds, and environmental interference. In this paper, we enhance high-frequency features of GEO targets while suppressing background noise at the single-frame level through wavelet transform. Building on this, we propose a multi-frame temporal trajectory completion scheme centered on the Hungarian algorithm for globally optimal cross-frame matching. To effectively mitigate missing and false detections, a series of key steps including temporal matching and interpolation completion, temporal-consistency-based noise filtering, and progressive trajectory refinement are designed in the post-processing pipeline. Experimental results on the public SpotGEO dataset demonstrate the effectiveness of the proposed method, achieving an F_1 score of 90.14%. |
| title | Space Object Detection using Multi-frame Temporal Trajectory Completion Method |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2510.19220 |