A systematic review: computer vision algorithms in drone surveillance

Hamzah Fawzy, Anas Elbrawy, Moustafa Amr, Omar Eltanekhy, Esraa Khatab, Omar Shalash

Abstract


Drones have emerged as advanced Cyber-Physical Systems (CPSs) with significant potential in data collection and environmental monitoring. Their ability to operate via wireless communication channels makes them integral to various IoT applications, such as surveillance, delivery services, traffic monitoring, and precision agriculture. Drones surpass traditional surveillance methods by offering better mobility and broader coverage, enabling efficient decision-making in diverse contexts, However, object detection in drone-captured images poses challenges due to varying spatial resolutions, the large number of objects, and their diverse sizes in aerial imagery. This paper provides a comprehensive review of drone-based surveillance techniques, focusing on object detection and tracking algorithms, relevant datasets, and exploration strategies. By analyzing current methods and identifying key trends, this study aims to highlight advancements and opportunities for improving the performance and reliability of drone-based surveillance systems.

 

Received on, 23 December 2024

Accepted on, 19 February 2025

Published on, 03 March 2025


Keywords


Drones; Surveillance; Objects Detection; Objects Tracking

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References


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DOI: http://dx.doi.org/10.21622/RIMC.2025.02.1.1149

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Copyright (c) 2025 Hamzah Fawzy, Anas Elbrawy, Moustafa Amr, Omar Eltanekhy, Esraa Khatab, Omar Shalash

Robotics : Integration, Manufacturing and Control

E-ISSN: 3009-7967

P-ISSN: 3009-6987

 

Published by:

Academy Publishing Center (APC)

Arab Academy for Science, Technology and Maritime Transport (AASTMT)

Alexandria, Egypt

rimc@aast.edu