UTA-Sign: Unsupervised Thermal Video Augmentation via Event-Assisted Traffic Signage Sketching
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866909757762174976 |
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| author | Han, Yuqi Zhang, Songqian Su, Weijian Li, Ke Yang, Jiayu Suo, Jinli Zhang, Qiang |
| author_facet | Han, Yuqi Zhang, Songqian Su, Weijian Li, Ke Yang, Jiayu Suo, Jinli Zhang, Qiang |
| contents | The thermal camera excels at perceiving outdoor environments under low-light conditions, making it ideal for applications such as nighttime autonomous driving and unmanned navigation. However, thermal cameras encounter challenges when capturing signage from objects made of similar materials, which can pose safety risks for accurately understanding semantics in autonomous driving systems. In contrast, the neuromorphic vision camera, also known as an event camera, detects changes in light intensity asynchronously and has proven effective in high-speed, low-light traffic environments. Recognizing the complementary characteristics of these two modalities, this paper proposes UTA-Sign, an unsupervised thermal-event video augmentation for traffic signage in low-illumination environments, targeting elements such as license plates and roadblock indicators. To address the signage blind spots of thermal imaging and the non-uniform sampling of event cameras, we developed a dual-boosting mechanism that fuses thermal frames and event signals for consistent signage representation over time. The proposed method utilizes thermal frames to provide accurate motion cues as temporal references for aligning the uneven event signals. At the same time, event signals contribute subtle signage content to the raw thermal frames, enhancing the overall understanding of the environment. The proposed method is validated on datasets collected from real-world scenarios, demonstrating superior quality in traffic signage sketching and improved detection accuracy at the perceptual level. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_20594 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | UTA-Sign: Unsupervised Thermal Video Augmentation via Event-Assisted Traffic Signage Sketching Han, Yuqi Zhang, Songqian Su, Weijian Li, Ke Yang, Jiayu Suo, Jinli Zhang, Qiang Computer Vision and Pattern Recognition The thermal camera excels at perceiving outdoor environments under low-light conditions, making it ideal for applications such as nighttime autonomous driving and unmanned navigation. However, thermal cameras encounter challenges when capturing signage from objects made of similar materials, which can pose safety risks for accurately understanding semantics in autonomous driving systems. In contrast, the neuromorphic vision camera, also known as an event camera, detects changes in light intensity asynchronously and has proven effective in high-speed, low-light traffic environments. Recognizing the complementary characteristics of these two modalities, this paper proposes UTA-Sign, an unsupervised thermal-event video augmentation for traffic signage in low-illumination environments, targeting elements such as license plates and roadblock indicators. To address the signage blind spots of thermal imaging and the non-uniform sampling of event cameras, we developed a dual-boosting mechanism that fuses thermal frames and event signals for consistent signage representation over time. The proposed method utilizes thermal frames to provide accurate motion cues as temporal references for aligning the uneven event signals. At the same time, event signals contribute subtle signage content to the raw thermal frames, enhancing the overall understanding of the environment. The proposed method is validated on datasets collected from real-world scenarios, demonstrating superior quality in traffic signage sketching and improved detection accuracy at the perceptual level. |
| title | UTA-Sign: Unsupervised Thermal Video Augmentation via Event-Assisted Traffic Signage Sketching |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.20594 |