Event-based multi-view photogrammetry for high-dynamic, high-velocity target measurement
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866910978230190080 |
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| author | Lei, Taihang Guan, Banglei Liang, Minzu Li, Xiangyu Liu, Jianbing Tao, Jing Shang, Yang Yu, Qifeng |
| author_facet | Lei, Taihang Guan, Banglei Liang, Minzu Li, Xiangyu Liu, Jianbing Tao, Jing Shang, Yang Yu, Qifeng |
| contents | The characterization of mechanical properties for high-dynamic, high-velocity target motion is essential in industries. It provides crucial data for validating weapon systems and precision manufacturing processes etc. However, existing measurement methods face challenges such as limited dynamic range, discontinuous observations, and high costs. This paper presents a new approach leveraging an event-based multi-view photogrammetric system, which aims to address the aforementioned challenges. First, the monotonicity in the spatiotemporal distribution of events is leveraged to extract the target's leading-edge features, eliminating the tailing effect that complicates motion measurements. Then, reprojection error is used to associate events with the target's trajectory, providing more data than traditional intersection methods. Finally, a target velocity decay model is employed to fit the data, enabling accurate motion measurements via ours multi-view data joint computation. In a light gas gun fragment test, the proposed method showed a measurement deviation of 4.47% compared to the electromagnetic speedometer. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_00578 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Event-based multi-view photogrammetry for high-dynamic, high-velocity target measurement Lei, Taihang Guan, Banglei Liang, Minzu Li, Xiangyu Liu, Jianbing Tao, Jing Shang, Yang Yu, Qifeng Computer Vision and Pattern Recognition The characterization of mechanical properties for high-dynamic, high-velocity target motion is essential in industries. It provides crucial data for validating weapon systems and precision manufacturing processes etc. However, existing measurement methods face challenges such as limited dynamic range, discontinuous observations, and high costs. This paper presents a new approach leveraging an event-based multi-view photogrammetric system, which aims to address the aforementioned challenges. First, the monotonicity in the spatiotemporal distribution of events is leveraged to extract the target's leading-edge features, eliminating the tailing effect that complicates motion measurements. Then, reprojection error is used to associate events with the target's trajectory, providing more data than traditional intersection methods. Finally, a target velocity decay model is employed to fit the data, enabling accurate motion measurements via ours multi-view data joint computation. In a light gas gun fragment test, the proposed method showed a measurement deviation of 4.47% compared to the electromagnetic speedometer. |
| title | Event-based multi-view photogrammetry for high-dynamic, high-velocity target measurement |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.00578 |