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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.00204 |
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| _version_ | 1866918136251416576 |
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| author | Tagiew, Rustam Wunderlich, Ilkay Sastuba, Mark Göller, Kilian Seitz, Steffen |
| author_facet | Tagiew, Rustam Wunderlich, Ilkay Sastuba, Mark Göller, Kilian Seitz, Steffen |
| contents | Driverless train operation for open tracks on urban guided transport and mainline railways requires, among other things automatic detection of actual and potential obstacles, especially humans, in the danger zone of the train's path. Machine learning algorithms have proven to be powerful state-of-the-art tools for this task. However, these algorithms require large amounts of high-quality annotated data containing human beings in railway-specific environments as training data. Unfortunately, the amount of publicly available datasets is not yet sufficient and is significantly inferior to the datasets in the road domain. Therefore, this paper presents RailGoerl24, an on-board visual light Full HD camera dataset of 12205 frames recorded in a railway test center of TÜV SÜD Rail, in Görlitz, Germany. Its main purpose is to support the development of driverless train operation for guided transport. RailGoerl24 also includes a terrestrial LiDAR scan covering parts of the area used to acquire the RGB data. In addition to the raw data, the dataset contains 33556 boxwise annotations in total for the object class 'person'. The faces of recorded actors are not blurred or altered in any other way. RailGoerl24, available at data.fid-move.de/dataset/railgoerl24, can also be used for tasks beyond collision prediction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_00204 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | RailGoerl24: Görlitz Rail Test Center CV Dataset 2024 Tagiew, Rustam Wunderlich, Ilkay Sastuba, Mark Göller, Kilian Seitz, Steffen Computer Vision and Pattern Recognition Artificial Intelligence Driverless train operation for open tracks on urban guided transport and mainline railways requires, among other things automatic detection of actual and potential obstacles, especially humans, in the danger zone of the train's path. Machine learning algorithms have proven to be powerful state-of-the-art tools for this task. However, these algorithms require large amounts of high-quality annotated data containing human beings in railway-specific environments as training data. Unfortunately, the amount of publicly available datasets is not yet sufficient and is significantly inferior to the datasets in the road domain. Therefore, this paper presents RailGoerl24, an on-board visual light Full HD camera dataset of 12205 frames recorded in a railway test center of TÜV SÜD Rail, in Görlitz, Germany. Its main purpose is to support the development of driverless train operation for guided transport. RailGoerl24 also includes a terrestrial LiDAR scan covering parts of the area used to acquire the RGB data. In addition to the raw data, the dataset contains 33556 boxwise annotations in total for the object class 'person'. The faces of recorded actors are not blurred or altered in any other way. RailGoerl24, available at data.fid-move.de/dataset/railgoerl24, can also be used for tasks beyond collision prediction. |
| title | RailGoerl24: Görlitz Rail Test Center CV Dataset 2024 |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2504.00204 |