A Fully Interpretable Statistical Approach for Roadside LiDAR Background Subtraction
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908837970182144 |
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| author | Iglesias, Aitor Aranjuelo, Nerea Javierre, Patricia Menendez, Ainhoa Arganda-Carreras, Ignacio Nieto, Marcos |
| author_facet | Iglesias, Aitor Aranjuelo, Nerea Javierre, Patricia Menendez, Ainhoa Arganda-Carreras, Ignacio Nieto, Marcos |
| contents | We present a fully interpretable and flexible statistical method for background subtraction in roadside LiDAR data, aimed at enhancing infrastructure-based perception in automated driving. Our approach introduces both a Gaussian distribution grid (GDG), which models the spatial statistics of the background using background-only scans, and a filtering algorithm that uses this representation to classify LiDAR points as foreground or background. The method supports diverse LiDAR types, including multiline 360 degree and micro-electro-mechanical systems (MEMS) sensors, and adapts to various configurations. Evaluated on the publicly available RCooper dataset, it outperforms state-of-the-art techniques in accuracy and flexibility, even with minimal background data. Its efficient implementation ensures reliable performance on low-resource hardware, enabling scalable real-world deployment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_22390 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Fully Interpretable Statistical Approach for Roadside LiDAR Background Subtraction Iglesias, Aitor Aranjuelo, Nerea Javierre, Patricia Menendez, Ainhoa Arganda-Carreras, Ignacio Nieto, Marcos Computer Vision and Pattern Recognition We present a fully interpretable and flexible statistical method for background subtraction in roadside LiDAR data, aimed at enhancing infrastructure-based perception in automated driving. Our approach introduces both a Gaussian distribution grid (GDG), which models the spatial statistics of the background using background-only scans, and a filtering algorithm that uses this representation to classify LiDAR points as foreground or background. The method supports diverse LiDAR types, including multiline 360 degree and micro-electro-mechanical systems (MEMS) sensors, and adapts to various configurations. Evaluated on the publicly available RCooper dataset, it outperforms state-of-the-art techniques in accuracy and flexibility, even with minimal background data. Its efficient implementation ensures reliable performance on low-resource hardware, enabling scalable real-world deployment. |
| title | A Fully Interpretable Statistical Approach for Roadside LiDAR Background Subtraction |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2510.22390 |