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

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

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 eld of view of a single camera necessitates multi-camera congurations to match UAVs across viewpoints { a problem known as re-identication (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 dierences in scale and pose. We propose the rst UAV re-identication 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 dierent Re-ID deep learning based approaches and their variants, spanning both convolutional and transformer architectures. Under the optimal conguration, such approaches are suciently powerful to learn a well-performing representation for UAV (81.9% mAP for Temporally-Near, 46.5% for the more dicult Big-to- Small challenge), while vision transformers are the most robust to extreme variance of scale.

Citation

Organisciak, D., Poyser, M., Alsehaim, A., Hu, S., Isaac-Medina, B. K., Breckon, T. P., & Shum, H. P. (2022). UAV-ReID: A Benchmark on Unmanned Aerial Vehicle Re-Identification in Video Imagery. . https://doi.org/10.5220/0010836600003124

• Conference Name: 2022 17th International Conference on Computer Vision Theory and Applications
• Acceptance Date: Nov 16, 2021
• Publication Date: 2022
• Deposit Date: Nov 23, 2021
• Publicly Available Date: Nov 23, 2021
• Pages: 136-146
• Series ISSN: 2184-4321
• ISBN: 9789897585555
• DOI: https://doi.org/10.5220/0010836600003124
• Keywords: Drone, UAV, Re-ID, Tracking, Deep Learning, Convolutional Neural Network