Drone Swarm for Distributed Video Surveillance of Roads and Car Tracking
This study proposes a swarm-based Unmanned Aerial Vehicle (UAV) system designed for surveillance tasks, specifically for detecting and tracking ground vehicles. The proposal is to assess how a system consisting of multiple cooperating UAVs can enhance performance by utilizing fast detection algorith...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-11-01
|
| Series: | Drones |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-446X/8/11/695 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850144987864367104 |
|---|---|
| author | David Sánchez Pedroche Daniel Amigo Jesús García José M. Molina Pablo Zubasti |
| author_facet | David Sánchez Pedroche Daniel Amigo Jesús García José M. Molina Pablo Zubasti |
| author_sort | David Sánchez Pedroche |
| collection | DOAJ |
| description | This study proposes a swarm-based Unmanned Aerial Vehicle (UAV) system designed for surveillance tasks, specifically for detecting and tracking ground vehicles. The proposal is to assess how a system consisting of multiple cooperating UAVs can enhance performance by utilizing fast detection algorithms. Within the study, the differences in one-stage and two-stage detection models have been considered, revealing that while two-stage models offer improved accuracy, their increased computation time renders them impractical for real-time applications. Consequently, faster one-stage models, such as the tested YOLOv8 architectures, appear to be a more viable option for real-time operations. Notably, the swarm-based approach enables these faster algorithms to achieve an accuracy level comparable to that of slower models. Overall, the experimentation analysis demonstrates how larger YOLO architectures exhibit longer processing times in exchange for superior tracking success rates. However, the inclusion of additional UAVs introduced in the system outweighed the choice of the tracking algorithm if the mission is correctly configured, thus demonstrating that the swarm-based approach facilitates the use of faster algorithms while maintaining performance levels comparable to slower alternatives. However, the perspectives provided by the included UAVs hold additional significance, as they are essential for achieving enhanced results. |
| format | Article |
| id | doaj-art-dfe74009d58a4ae0aa8a8e394009fa97 |
| institution | OA Journals |
| issn | 2504-446X |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj-art-dfe74009d58a4ae0aa8a8e394009fa972025-08-20T02:28:12ZengMDPI AGDrones2504-446X2024-11-0181169510.3390/drones8110695Drone Swarm for Distributed Video Surveillance of Roads and Car TrackingDavid Sánchez Pedroche0Daniel Amigo1Jesús García2José M. Molina3Pablo Zubasti4Applied Artificial Intelligence Research Group, Computer Science and Engineering Department, University Carlos III of Madrid, 28270 Colmenarejo, SpainEuropean Organisation for the Safety of Air Navigation, EUROCONTROL, 1130 Brussels, BelgiumApplied Artificial Intelligence Research Group, Computer Science and Engineering Department, University Carlos III of Madrid, 28270 Colmenarejo, SpainApplied Artificial Intelligence Research Group, Computer Science and Engineering Department, University Carlos III of Madrid, 28270 Colmenarejo, SpainIndependent Researcher, 28270 Colmenarejo, SpainThis study proposes a swarm-based Unmanned Aerial Vehicle (UAV) system designed for surveillance tasks, specifically for detecting and tracking ground vehicles. The proposal is to assess how a system consisting of multiple cooperating UAVs can enhance performance by utilizing fast detection algorithms. Within the study, the differences in one-stage and two-stage detection models have been considered, revealing that while two-stage models offer improved accuracy, their increased computation time renders them impractical for real-time applications. Consequently, faster one-stage models, such as the tested YOLOv8 architectures, appear to be a more viable option for real-time operations. Notably, the swarm-based approach enables these faster algorithms to achieve an accuracy level comparable to that of slower models. Overall, the experimentation analysis demonstrates how larger YOLO architectures exhibit longer processing times in exchange for superior tracking success rates. However, the inclusion of additional UAVs introduced in the system outweighed the choice of the tracking algorithm if the mission is correctly configured, thus demonstrating that the swarm-based approach facilitates the use of faster algorithms while maintaining performance levels comparable to slower alternatives. However, the perspectives provided by the included UAVs hold additional significance, as they are essential for achieving enhanced results.https://www.mdpi.com/2504-446X/8/11/695UAV surveillancevehicle detection and trackingUAV swarm configuration |
| spellingShingle | David Sánchez Pedroche Daniel Amigo Jesús García José M. Molina Pablo Zubasti Drone Swarm for Distributed Video Surveillance of Roads and Car Tracking Drones UAV surveillance vehicle detection and tracking UAV swarm configuration |
| title | Drone Swarm for Distributed Video Surveillance of Roads and Car Tracking |
| title_full | Drone Swarm for Distributed Video Surveillance of Roads and Car Tracking |
| title_fullStr | Drone Swarm for Distributed Video Surveillance of Roads and Car Tracking |
| title_full_unstemmed | Drone Swarm for Distributed Video Surveillance of Roads and Car Tracking |
| title_short | Drone Swarm for Distributed Video Surveillance of Roads and Car Tracking |
| title_sort | drone swarm for distributed video surveillance of roads and car tracking |
| topic | UAV surveillance vehicle detection and tracking UAV swarm configuration |
| url | https://www.mdpi.com/2504-446X/8/11/695 |
| work_keys_str_mv | AT davidsanchezpedroche droneswarmfordistributedvideosurveillanceofroadsandcartracking AT danielamigo droneswarmfordistributedvideosurveillanceofroadsandcartracking AT jesusgarcia droneswarmfordistributedvideosurveillanceofroadsandcartracking AT josemmolina droneswarmfordistributedvideosurveillanceofroadsandcartracking AT pablozubasti droneswarmfordistributedvideosurveillanceofroadsandcartracking |