Tracking Drones Across Different Platforms with Machine Vision
Security Technology Research Innovation Grants Programme (S-TRIG)

Funding Source: The Catapult Network (S-TRIG), UK
Reference Number: 007CD
Value: £32,727
2020 - 2021

About the Project

Provided the UK government with extra capacity to identify and counter hostile drones (see YouTube demonstration).
Supported a research project with Chris G. Willcocks, General Autonomy Ltd., Toby P. Breckon and Daniel Organisciak.
Recruited two Research Assistants Brian K. S. Isaac-Medina and Matthew Poyser for developing the project.
Interviewed by the UK Catapult Network on how AI and computer vision enhance security.
Demonstrated project outcomes in the TRIG showcase event.

Unmanned Aerial Vehicle Visual Detection and Tracking using Deep Neural Networks: A Performance Benchmark H5-Index: 80^# Citation: 75^#
Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), 2021
Brian K. S. Isaac-Medina, Matthew Poyser, Daniel Organisciak, Chris G. Willcocks, Toby P. Breckon and Hubert P. H. Shum
Topics: Surveillance, Video Analysis, Datasets
Webpage Cite This Paper GitHub

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
Daniel Organisciak, Matthew Poyser, Aishah Alsehaim, Shanfeng Hu, Brian K. S. Isaac-Medina, Toby P. Breckon and Hubert P. H. Shum
Topics: Surveillance, Datasets
Webpage Cite This Paper GitHub