Adversarial Appearance Learning in Augmented Cityscapes for Pedestrian Recognition in Autonomous Driving
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866909791881789440 |
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| author | Savkin, Artem Lapotre, Thomas Strauss, Kevin Akbar, Uzair Tombari, Federico |
| author_facet | Savkin, Artem Lapotre, Thomas Strauss, Kevin Akbar, Uzair Tombari, Federico |
| contents | In the autonomous driving area synthetic data is crucial for cover specific traffic scenarios which autonomous vehicle must handle. This data commonly introduces domain gap between synthetic and real domains. In this paper we deploy data augmentation to generate custom traffic scenarios with VRUs in order to improve pedestrian recognition. We provide a pipeline for augmentation of the Cityscapes dataset with virtual pedestrians. In order to improve augmentation realism of the pipeline we reveal a novel generative network architecture for adversarial learning of the data-set lighting conditions. We also evaluate our approach on the tasks of semantic and instance segmentation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_13507 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Adversarial Appearance Learning in Augmented Cityscapes for Pedestrian Recognition in Autonomous Driving Savkin, Artem Lapotre, Thomas Strauss, Kevin Akbar, Uzair Tombari, Federico Computer Vision and Pattern Recognition In the autonomous driving area synthetic data is crucial for cover specific traffic scenarios which autonomous vehicle must handle. This data commonly introduces domain gap between synthetic and real domains. In this paper we deploy data augmentation to generate custom traffic scenarios with VRUs in order to improve pedestrian recognition. We provide a pipeline for augmentation of the Cityscapes dataset with virtual pedestrians. In order to improve augmentation realism of the pipeline we reveal a novel generative network architecture for adversarial learning of the data-set lighting conditions. We also evaluate our approach on the tasks of semantic and instance segmentation. |
| title | Adversarial Appearance Learning in Augmented Cityscapes for Pedestrian Recognition in Autonomous Driving |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.13507 |