Leveraging Unreal Engine for UAV Object Tracking: The AirTrackSynth Synthetic Dataset

Mingyang Zhang, Kristof Van Beeck, Toon Goedemé

2025

Abstract

Nowadays, synthetic datasets are often used to advance the state-of-the-art in many application domains of computer vision. For these tasks, deep learning approaches are used which require vasts amounts of data. Acquiring these large annotated datasets is far from trivial, since it is very time-consuming, expensive and prone to errors during the labelling process. These synthetic datasets aim to offer solutions to the aforementioned problems. In this paper, we introduce our AirTrackSynth dataset, developed to train and evaluate deep learning models for UAV object tracking. This dataset, created using the Unreal Engine and AirSim, comprises 300GB of data in 200 well-structured video sequences. AirTrackSynth is notable for its extensive variety of objects and complex environments, setting a new standard in the field. This dataset is characterized by its multi-modal sensor data, accurate ground truth labels and a variety of environmental conditions, including distinct weather patterns, lighting conditions, and challenging viewpoints, thereby offering a rich platform to train robust object tracking models. Through the evaluation of the SiamFC object tracking algorithm on Air-TrackSynth, we demonstrate the dataset’s ability to present substantial challenges to existing methodologies and notably highlight the importance of synthetic data, especially when the availability of real data is limited. This enhancement in algorithmic performance under diverse and complex conditions underscores the critical role of synthetic data in developing advanced tracking technologies.

Paper Citation

in Harvard Style

Zhang M., Van Beeck K. and Goedemé T. (2025). Leveraging Unreal Engine for UAV Object Tracking: The AirTrackSynth Synthetic Dataset. In Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP; ISBN 978-989-758-728-3, SciTePress, pages 743-750. DOI: 10.5220/0013319400003912

in Bibtex Style

@conference{visapp25,
author={Mingyang Zhang and Kristof Van Beeck and Toon Goedemé},
title={Leveraging Unreal Engine for UAV Object Tracking: The AirTrackSynth Synthetic Dataset},
booktitle={Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP},
year={2025},
pages={743-750},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013319400003912},
isbn={978-989-758-728-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP
TI - Leveraging Unreal Engine for UAV Object Tracking: The AirTrackSynth Synthetic Dataset
SN - 978-989-758-728-3
AU - Zhang M.
AU - Van Beeck K.
AU - Goedemé T.
PY - 2025
SP - 743
EP - 750
DO - 10.5220/0013319400003912
PB - SciTePress