CattleFace-RGBT: RGB-T Cattle Facial Landmark Benchmark
To address this challenge, we introduce CattleFace-RGBT, a RGB-T Cattle Facial Landmark dataset consisting of 2,300 RGB-T image pairs, a total of 4,600 images. Creating a landmark dataset is time-consuming, but AI-assisted annotation can help. However, applying AI to thermal images is challenging du...
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Zusammenfassung: | To address this challenge, we introduce CattleFace-RGBT, a RGB-T Cattle
Facial Landmark dataset consisting of 2,300 RGB-T image pairs, a total of 4,600
images. Creating a landmark dataset is time-consuming, but AI-assisted
annotation can help. However, applying AI to thermal images is challenging due
to suboptimal results from direct thermal training and infeasible RGB-thermal
alignment due to different camera views. Therefore, we opt to transfer models
trained on RGB to thermal images and refine them using our AI-assisted
annotation tool following a semi-automatic annotation approach. Accurately
localizing facial key points on both RGB and thermal images enables us to not
only discern the cattle's respiratory signs but also measure temperatures to
assess the animal's thermal state. To the best of our knowledge, this is the
first dataset for the cattle facial landmark on RGB-T images. We conduct
benchmarking of the CattleFace-RGBT dataset across various backbone
architectures, with the objective of establishing baselines for future
research, analysis, and comparison. The dataset and models are at
https://github.com/UARK-AICV/CattleFace-RGBT-benchmark |
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DOI: | 10.48550/arxiv.2406.03431 |