| Hou, Xinyu, Beuchert, Jonas, Shin, Sangyun, Markham, Andrew and Trigoni, Niki 2025. Thermal-to-RGB video translation for wildlife monitoring: Enhancing low-resolution thermal imagery with large diffusion models. Presented at: HotSense '25, Hong Kong, China, 4-8 November 2025. Proceedings of the 2025 ACM International Workshop on Thermal Sensing and Computing. New York, NY: Association for Computing Machinery, pp. 13-18. 10.1145/3737905.3769285 |
Abstract
Wildlife monitoring frequently relies on thermal cameras to capture animal activity in low-light or nocturnal conditions, as many species are primarily active at night. However, high-resolution thermal cameras are significantly more expensive than RGB cameras of comparable resolution, while low-cost thermal sensors generally produce low-resolution, noisy imagery, limiting interpretability for human operators and downstream machine learning tasks. We introduce the first framework for spatially and temporally consistent super-resolution RGB video generation from low-resolution thermal inputs, leveraging large diffusion models. Our pipeline operates in a fully tuning-free manner and is qualitatively evaluated across diverse animal species, datasets, thermal cameras, and resolutions, demonstrating strong generalization. The generated RGB outputs enhance scene interpretability while preserving temporal coherence, offering a practical tool for wildlife conservation, anti-poaching efforts, and behavioral analysis.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Date Type: | Published Online |
| Status: | Published |
| Schools: | Schools > Computer Science & Informatics |
| Publisher: | Association for Computing Machinery |
| ISBN: | 9798400719820 |
| Date of Acceptance: | 1 September 2025 |
| Last Modified: | 20 Oct 2025 11:00 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/181744 |
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