UAV-ReID: A Benchmark on Unmanned Aerial Vehicle Re-identification in Video Imagery

Daniel Organisciak, Matthew Poyser, Aishah Alsehaim, Shanfeng Hu, Brian K. S. Isaac-Medina, Toby P. Breckon, Hubert P. H. Shum

2022

Abstract

As unmanned aerial vehicles (UAV) become more accessible with a growing range of applications, the risk of UAV disruption increases. Recent development in deep learning allows vision-based counter-UAV systems to detect and track UAVs with a single camera. However, the limited field of view of a single camera necessitates multi-camera configurations to match UAVs across viewpoints – a problem known as re-identification (Re- ID). While there has been extensive research on person and vehicle Re-ID to match objects across time and viewpoints, to the best of our knowledge, UAV Re-ID remains unresearched but challenging due to great differences in scale and pose. We propose the first UAV re-identification data set, UAV-reID, to facilitate the development of machine learning solutions in multi-camera environments. UAV-reID has two sub-challenges: Temporally-Near and Big-to-Small to evaluate Re-ID performance across viewpoints and scale respectively. We conduct a benchmark study by extensively evaluating different Re-ID deep learning based approaches and their variants, spanning both convolutional and transformer architectures. Under the optimal configuration, such approaches are sufficiently powerful to learn a well-performing representation for UAV (81.9% mAP for Temporally-Near, 46.5% for the more difficult Big-to-Small challenge), while vision transformers are the most robust to extreme variance of scale.

Paper Citation

in Harvard Style

Organisciak D., Poyser M., Alsehaim A., Hu S., Isaac-Medina B., Breckon T. and Shum H. (2022). UAV-ReID: A Benchmark on Unmanned Aerial Vehicle Re-identification in Video Imagery. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP; ISBN 978-989-758-555-5, SciTePress, pages 136-146. DOI: 10.5220/0010836600003124

in Bibtex Style

@conference{visapp22,
author={Daniel Organisciak and Matthew Poyser and Aishah Alsehaim and Shanfeng Hu and Brian K. S. Isaac-Medina and Toby P. Breckon and Hubert P. H. Shum},
title={UAV-ReID: A Benchmark on Unmanned Aerial Vehicle Re-identification in Video Imagery},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP},
year={2022},
pages={136-146},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010836600003124},
isbn={978-989-758-555-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP
TI - UAV-ReID: A Benchmark on Unmanned Aerial Vehicle Re-identification in Video Imagery
SN - 978-989-758-555-5
AU - Organisciak D.
AU - Poyser M.
AU - Alsehaim A.
AU - Hu S.
AU - Isaac-Medina B.
AU - Breckon T.
AU - Shum H.
PY - 2022
SP - 136
EP - 146
DO - 10.5220/0010836600003124
PB - SciTePress