A systematic review: computer vision algorithms in drone surveillance
<p class="p1">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,...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Academy Publishing Center
2025-03-01
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| Series: | Robotics: Integration, Manufacturing and Control |
| Subjects: | |
| Online Access: | http://apc.aast.edu/ojs/index.php/RIMC/article/view/1149 |
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| author | Hamzah Fawzy Anas Elbrawy Moustafa Amr Omar Eltanekhy Esraa Khatab Omar Shalash |
| author_facet | Hamzah Fawzy Anas Elbrawy Moustafa Amr Omar Eltanekhy Esraa Khatab Omar Shalash |
| author_sort | Hamzah Fawzy |
| collection | DOAJ |
| description | <p class="p1">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.</p><p class="p1"> </p><p class="p1"><strong>Received on, 23 December 2024 </strong></p><p class="p1"><strong>Accepted on, 19 February 2025 </strong></p><p class="p1"><strong>Published on, 03 March 2025</strong></p> |
| format | Article |
| id | doaj-art-41b8d050023a412682cdd5bd8d5a5bfc |
| institution | OA Journals |
| issn | 3009-6987 3009-7967 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Academy Publishing Center |
| record_format | Article |
| series | Robotics: Integration, Manufacturing and Control |
| spelling | doaj-art-41b8d050023a412682cdd5bd8d5a5bfc2025-08-20T02:37:20ZengAcademy Publishing CenterRobotics: Integration, Manufacturing and Control3009-69873009-79672025-03-012111010.21622/RIMC.2025.02.1.1149461A systematic review: computer vision algorithms in drone surveillanceHamzah Fawzy0Anas Elbrawy1Moustafa Amr2Omar Eltanekhy3Esraa Khatab4Omar Shalash5College of Artificial Intelligence, Arab Academy for Science and Technology and Maritime Transport, New AlameinCollege of Artificial Intelligence, Arab Academy for Science and Technology and Maritime Transport, New AlameinCollege of Artificial Intelligence, Arab Academy for Science and Technology and Maritime Transport, New AlameinCollege of Artificial Intelligence, Arab Academy for Science and Technology and Maritime Transport, New AlameinSchool of Mathematics and Computer Science Herriot Watt University, Dubai Knowledge Park, DubaiCollege of Artificial Intelligence, Arab Academy for Science and Technology and Maritime Transport, New Alamein<p class="p1">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.</p><p class="p1"> </p><p class="p1"><strong>Received on, 23 December 2024 </strong></p><p class="p1"><strong>Accepted on, 19 February 2025 </strong></p><p class="p1"><strong>Published on, 03 March 2025</strong></p>http://apc.aast.edu/ojs/index.php/RIMC/article/view/1149dronessurveillanceobjects detectionobjects tracking |
| spellingShingle | Hamzah Fawzy Anas Elbrawy Moustafa Amr Omar Eltanekhy Esraa Khatab Omar Shalash A systematic review: computer vision algorithms in drone surveillance Robotics: Integration, Manufacturing and Control drones surveillance objects detection objects tracking |
| title | A systematic review: computer vision algorithms in drone surveillance |
| title_full | A systematic review: computer vision algorithms in drone surveillance |
| title_fullStr | A systematic review: computer vision algorithms in drone surveillance |
| title_full_unstemmed | A systematic review: computer vision algorithms in drone surveillance |
| title_short | A systematic review: computer vision algorithms in drone surveillance |
| title_sort | systematic review computer vision algorithms in drone surveillance |
| topic | drones surveillance objects detection objects tracking |
| url | http://apc.aast.edu/ojs/index.php/RIMC/article/view/1149 |
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