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

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

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.

Publication

Daniel Organisciak, Matthew Poyser, Aishah Alsehaim, Shanfeng Hu, Brian K. S. Isaac-Medina, Toby P. Breckon and Hubert P. H. Shum,
"UAV-ReID: A Benchmark on Unmanned Aerial Vehicle Re-identification in Video Imagery",
Proceedings of the 2022 International Conference on Computer Vision Theory and Applications (VISAPP)
, 2022
Citation: 1##

## Citation counts from Google Scholar as of 2022

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References

BibTeX

@inproceedings{organisciak22uavreid,
 author={Organisciak, Daniel and Poyser, Matthew and Alsehaim, Aishah and Hu, Shanfeng and Isaac-Medina, Brian K. S. and Breckon, Toby P. and Shum, Hubert P. H.},
 booktitle={Proceedings of the 2022 International Conference on Computer Vision Theory and Applications},
 series={VISAPP '21},
 title={UAV-ReID: A Benchmark on Unmanned Aerial Vehicle Re-identification in Video Imagery},
 year={2022},
 month={Feb},
 pages={136--146},
 numpages={11},
 doi={10.5220/0010836600003124},
 isbn={978-989-758-555-5},
 publisher={SciTePress},
}

RIS

TY  - CONF
AU  - Organisciak, Daniel
AU  - Poyser, Matthew
AU  - Alsehaim, Aishah
AU  - Hu, Shanfeng
AU  - Isaac-Medina, Brian K. S.
AU  - Breckon, Toby P.
AU  - Shum, Hubert P. H.
T2  - Proceedings of the 2022 International Conference on Computer Vision Theory and Applications
TI  - UAV-ReID: A Benchmark on Unmanned Aerial Vehicle Re-identification in Video Imagery
PY  - 2022
Y1  - Feb 2022
SP  - 136
EP  - 146
DO  - 10.5220/0010836600003124
SN  - 978-989-758-555-5
PB  - SciTePress
ER  - 

Plain Text

Daniel Organisciak, Matthew Poyser, Aishah Alsehaim, Shanfeng Hu, Brian K. S. Isaac-Medina, Toby P. Breckon and Hubert P. H. Shum, "UAV-ReID: A Benchmark on Unmanned Aerial Vehicle Re-identification in Video Imagery," in VISAPP '21: Proceedings of the 2022 International Conference on Computer Vision Theory and Applications, pp. 136-146, SciTePress, Feb 2022.

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