---
task_categories:
- token-classification
language:
- en
tags:
- automobile
- sensor
pretty_name: vot-rgbt
size_categories:
- 100M<n<1B
---

# 🚁 VOT-RGBT 2019 Challenge Dataset

**Visual Object Tracking for RGB and Thermal Imagery**

---

## Dataset Summary

The **VOT-RGBT 2019** dataset is part of the [Visual Object Tracking](http://www.votchallenge.net/) (VOT) initiative — a series of challenges that provide the computer vision community with standardized benchmarks for evaluating **short-term** and **long-term visual object trackers**. This particular edition focuses on **RGB-T (Visible + Thermal)** imagery, encouraging robust object tracking in challenging multimodal environments.

---

## Supported Tasks and Leaderboards

- **📦 Visual Object Tracking (Short-Term & Long-Term)**
  - Track object location across RGB and thermal frames
  - Evaluate robustness to occlusion, illumination changes, and environmental conditions

---

## Dataset Structure

- **Sequences**: 60+ sequences with aligned RGB and thermal imagery
- **Annotations**: Per-frame ground truth bounding boxes in both modalities
- **Modalities**: RGB, Thermal (LWIR)
- **Frame Rate**: 20-30 FPS (varies by sequence)
- **Resolution**: Varies by sensor and sequence (mostly HD and VGA)

---

## Usage

To use the dataset:

```python
from datasets import load_dataset
# This assumes dataset is hosted on Hugging Face datasets hub
dataset = load_dataset("langutang/vot-rgbt2019")
```

Alternatively, you can download it from the [official VOT site](http://www.votchallenge.net/vot2019/).

---

## Evaluation Protocol

VOT-RGBT 2019 uses the standard VOT evaluation protocol:

- **Accuracy (A)**: Overlap between predicted and ground truth bounding boxes
- **Robustness (R)**: Number of tracking failures
- **Expected Average Overlap (EAO)**: Combines A and R for a unified score

Both short-term and long-term tracker performance can be evaluated using the provided toolkit.

---

## Citation

If you use this dataset in your research, please cite:

```
@inproceedings{votrgbt2019,
  title={RGB-Thermal Object Tracking: Benchmark and Baselines},
  author={Liang, Jianan and Hu, Jiakai and Zhang, Yu and others},
  booktitle={ECCV Workshops},
  year={2019}
}
```

---

## License

Please refer to the license and usage terms outlined on the official VOT website.

---

## Acknowledgements

Thanks to the VOT committee and contributing authors for their continued efforts in pushing forward the field of visual object tracking.

---

## Tags

`computer-vision`, `object-tracking`, `rgbt`, `thermal-imaging`, `vot`, `multimodal`, `benchmark`