phamtrongthang/CattleFace-RGBT

CattleFace-RGBT: Cattle Facial Landmark Dataset with RGB-Thermal Imagery

Dataset Description

CattleFace-RGBT is the first publicly available multimodal dataset featuring paired frontal-view RGB and thermal facial images of cattle, annotated with 13 facial keypoints and associated ground-truth rectal temperature measurements. The dataset was developed to support research in automated cattle fever estimation and precision livestock farming.

Paper: CattleFever: An automated cattle fever estimation system Published in: Smart Agricultural Technology, Volume 12, 2025

Dataset Summary

Dataset Structure

CattleFace-RGBT/
├── README.md
├── rgb/                          # RGB images organized by folder
│   ├── 1/
│   ├── 17/
│   ├── 25/
│   ├── 50/
│   └── 64/
├── thermal/                      # Colorized thermal JPG images
│   ├── 1/
│   ├── 2/
│   ├── 17/
│   ├── 25/
│   ├── 50/
│   └── 64/
├── thermal_raw/                  # Raw thermal TIFF frames (temperature data)
│   ├── 02_01/                    # Feb 1 recording session
│   ├── 02_06/                    # Feb 6 recording session
│   └── 02_13/                    # Feb 13 recording session
└── annotations/
    ├── rgb_keypoints.json        # COCO-format keypoint annotations for RGB
    ├── thermal_keypoints.json    # COCO-format keypoint annotations for thermal
    ├── metadata.csv              # Cow ID, temperature, and data mapping
    └── cow_mapping.json          # Sequence number → cow tag ID mapping

Annotation Format

Annotations follow the COCO keypoint format:

Images

{
  "id": 0,
  "file_name": "rgb/1/00001.jpg",
  "width": 2560,
  "height": 1440,
  "folder": "1",
  "frame_id": "00001"
}

Annotations

{
  "id": 0,
  "image_id": 0,
  "category_id": 1,
  "keypoints": [x1, y1, v1, x2, y2, v2, ...],
  "num_keypoints": 13,
  "bbox": [x, y, width, height],
  "area": 123456,
  "iscrowd": 0
}

13 Facial Keypoints

Visibility flag: 0 = not labeled, 2 = labeled and visible.

Raw Thermal Data

The thermal_raw/ directory contains raw TIFF frames from the ICI FMX 400 thermal camera (384 x 288 pixels). Each pixel contains a temperature value in Celsius. These files can be read with:

from PIL import Image
import numpy as np

tiff = Image.open("thermal_raw/02_01/0001_Video_Frame_1.tiff")
temp_array = np.array(tiff)  # Temperature values in Celsius

TIFF filenames follow the pattern: {sequence_num}_Video_Frame_{frame_num}.tiff

Use cow_mapping.json to map sequence numbers to cow tag IDs and temperatures.

Temperature Data

Ground-truth rectal temperatures (in Fahrenheit) are available for 21 cattle across 3 recording sessions. The mapping is provided in metadata.csv and cow_mapping.json.

Data Collection

Data was collected at the Arkansas Agricultural Experiment Station, Savoy Research Complex, Beef Cattle Research Area, in partnership with the University of Arkansas. The setup used:

• —RGB camera: Standard webcam (2560 x 1440 resolution)
• —Thermal camera: ICI FMX 400 (384 x 288 pixel resolution, 50 Hz frame rate, < 0.03°C thermal sensitivity)
• —Temperature: Rectal thermometer (ground truth)

Each calf was guided into a cattle squeeze chute for ~20 seconds of synchronized RGB and thermal video recording.

Supported Tasks

1. 1.Cattle facial landmark detection — Detect 13 keypoints on cattle faces
2. 2.Cattle face detection — Detect and localize cattle faces using bounding boxes
3. 3.Core body temperature estimation — Predict rectal temperature from thermal facial features

Recommended Splits

As described in the paper:

• —Keypoint detection: 70% train / 30% test (random split)
• —Temperature estimation: 80% train / 20% test

Citation

@article{pham2025cattlefever,
  title={CattleFever: An automated cattle fever estimation system},
  author={Pham, Trong Thang and Coffman, Ethan and Kegley, Beth and Powell, Jeremy G. and Zhao, Jiangchao and Le, Ngan},
  journal={Smart Agricultural Technology},
  volume={12},
  pages={101434},
  year={2025},
  publisher={Elsevier},
  doi={10.1016/j.atech.2025.101434}
}

License

This dataset is released under the CC BY 4.0 license.

Contact

For questions about this dataset, please contact:

• —Trong Thang Pham (tp030@uark.edu) — AICV Lab, University of Arkansas