metadata

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 (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:

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.

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.

Datasets:

langutang
/

vot-rgbt-2019