PoseDriver: A Unified Approach to Multi-Category Skeleton Detection for Autonomous Driving
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866918407381712896 |
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| author | Borhani, Yasamin Mordan, Taylor Wang, Yihan Hosseininejad, Reyhaneh Khoramdel, Javad Alahi, Alexandre |
| author_facet | Borhani, Yasamin Mordan, Taylor Wang, Yihan Hosseininejad, Reyhaneh Khoramdel, Javad Alahi, Alexandre |
| contents | Object skeletons offer a concise representation of structural information, capturing essential aspects of posture and orientation that are crucial for autonomous driving applications. However, a unified architecture that simultaneously handles multiple instances and categories using only the input image remains elusive. In this paper, we introduce PoseDriver, a unified framework for bottom-up multi-category skeleton detection tailored to common objects in driving scenarios. We model each category as a distinct task to systematically address the challenges of multi-task learning. Specifically, we propose a novel approach for lane detection based on skeleton representations, achieving state-of-the-art performance on the OpenLane dataset. Moreover, we present a new dataset for bicycle skeleton detection and assess the transferability of our framework to novel categories. Experimental results validate the effectiveness of the proposed approach. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_23215 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | PoseDriver: A Unified Approach to Multi-Category Skeleton Detection for Autonomous Driving Borhani, Yasamin Mordan, Taylor Wang, Yihan Hosseininejad, Reyhaneh Khoramdel, Javad Alahi, Alexandre Computer Vision and Pattern Recognition Human-Computer Interaction Object skeletons offer a concise representation of structural information, capturing essential aspects of posture and orientation that are crucial for autonomous driving applications. However, a unified architecture that simultaneously handles multiple instances and categories using only the input image remains elusive. In this paper, we introduce PoseDriver, a unified framework for bottom-up multi-category skeleton detection tailored to common objects in driving scenarios. We model each category as a distinct task to systematically address the challenges of multi-task learning. Specifically, we propose a novel approach for lane detection based on skeleton representations, achieving state-of-the-art performance on the OpenLane dataset. Moreover, we present a new dataset for bicycle skeleton detection and assess the transferability of our framework to novel categories. Experimental results validate the effectiveness of the proposed approach. |
| title | PoseDriver: A Unified Approach to Multi-Category Skeleton Detection for Autonomous Driving |
| topic | Computer Vision and Pattern Recognition Human-Computer Interaction |
| url | https://arxiv.org/abs/2603.23215 |