The DOI matches its Crossref record; 12 of the 13 references it cites were found in a registry or an index.
AVSS: Airborne Video Surveillance System
Open accessAbstract
Most surveillance systems only contain CCTVs. CCTVs, however, provide only limited maneuverability against dynamic targets and are inefficient for short term surveillance. Such limitations do not raise much concern in some cases, but for the scenario in which traditional surveillance systems do not suffice, adopting a fleet of UAVs can help overcoming the limitations. In this paper, we present a surveillance system implemented with a fleet of unmanned aerial vehicles (UAVs). A surveillance system implemented with a fleet of UAVs is easy to deploy and maintain. A UAV fleet requires little time to deploy and set up, and removing the surveillance is also virtually instant. The system we propose deploys UAVs to the target area for installation and perform surveillance operations. The camera mounted UAVs act as surveillance probes, the server provides overall control of the surveillance system, and the fleet platform provides fleet-wise control of the UAVs. In the proposed system, the UAVs establish a network and enable multi-hop communication, which allows the system to widen its coverage area. The operator of the system can control the fleet of UAVs via the fleet platform and receive surveillance information gathered by the UAVs. The proposed system is described in detail along with the algorithm for effective placement of the UAVs. The prototype of the system is presented, and the experiment carried out shows that the system can successfully perform surveillance over an area set by the system.
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“Drones provide an aerial view; their real-time data and mobility make them valuable tools for enhancing security and improving various aspects of urban management, which are used popularly for purposes such as object and people detection, data collection, general traffic and wildlife monitoring, radio surveillance, and search and rescue operations during disasters [15][16][17][18].”
From A Survey of Video Surveillance Systems in Smart City (2023) · cites this paper as [15]
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Cites
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Survey on Anti-Drone Systems: Components, Designs, and Challenges
Park, S., Kim, H. T., Lee, S., Joo, H., & Kim, H. (2021) · IEEE Access · doi:10.1109/access.2021.3065926 · Cited by 332
Self-citationSwarm of UAVs for Network Management in 6G: A Technical Review
Khan, M. A., Kumar, N., Mohsan, S. A. H., Khan, W. U., Nasralla, M. M., Alsharif, M. H., Żywiołek, J., & Ullah, I. (2022) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2022.3213370 · Cited by 264
A Survey of Video Surveillance Systems in Smart City
Myagmar-Ochir, Y., & Kim, W. (2023) · Electronics · doi:10.3390/electronics12173567 · Cited by 134
“…popularly for such as object and people detection, data collection, general traffic and wildlife Crowd-sourcing based on smartphone cameras is not common, but it is a very innovative approach to monitoring the smart city. radio surveillance, and search and rescue operations during disasters [15–18].”
BackgroundDrone Detection and Identification System using Artificial Intelligence
Lee, D., La, W. G., & Kim, H. (2018) · International Conference on Information and Communication Technology Convergence (ICTC) · doi:10.1109/ictc.2018.8539442 · Cited by 120
Self-citationDRONET: Multi-Tasking Framework for Real-Time Industrial Facility Aerial Surveillance and Safety
Ajakwe, S. O., Ihekoronye, V. U., Kim, D., & Lee, J. (2022) · Drones · doi:10.3390/drones6020046 · Cited by 51
UAV Flight and Landing Guidance System for Emergency Situations †
Lee, J. Y., Chung, A. Y., Shim, H., Joe, C., Park, S., & Kim, H. (2019) · Sensors · doi:10.3390/s19204468 · Cited by 36
Self-citationFormation Control Algorithm of Multi-UAV-Based Network Infrastructure
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Self-citationAdaptive Drone Identification and Neutralization Scheme for Real-Time Military Tactical Operations
Ajakwe, S. O., Ihekoronye, V. U., Akter, R., Kim, D., & Lee, J. M. (2022) · International Conference on Information Networking · doi:10.1109/icoin53446.2022.9687268 · Cited by 22
Drone Positioning System Using UWB Sensing and Out-of-Band Control
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Self-citationAn MPTCP-Based Transmission Scheme for Improving the Control Stability of Unmanned Aerial Vehicles
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Self-citationA review of UAV Visual Detection and Tracking MethodsPreprint
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Secure Automated Video Assistance in Vehicular Networks using Unmanned Aerial Vehicles
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Self-citationEnhancing gas detection-based swarming through deep reinforcement learning
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Self-citationQoE Analysis of Real-Time Video Streaming over 4G-LTE for UAV-Based Surveillance Applications
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Metadata
- Authors
- Jung, J., Yoo, S., La, W. G., Lee, D. R., Bae, M., & Kim, H.
- Affiliations
- Korea University (KR)
- Corresponding author
- Kim, Hwangnam
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2018-06-14
- Volume
- 18
- Issue
- 6
- Pages
- 1939
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC6021840
- OpenAlex
- W2808172345
- Cited by
- 30
- References
- 13
- Citations by year
- 2026: 3 · 2023: 2 · 2022: 5 · 2021: 5 · 2020: 6 · 2019: 4 · 2018: 5
- FWCI
- 4.07
- Type
- journal article
- Language
- en
- Open access
- gold
- dx.doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Unmanned Vehicles Advanced Core Technology Research and Development Program (NRF-2016M1B3A1A01937599)
- Topics
- Physical Sciences › Engineering › Aerospace Engineering › UAV Applications and Optimization
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Computer Science › Computer Networks and Communications › Mobile Ad Hoc Networks
- Keywords
- UAV fleet, unmanned aerial vehicles, UAV deployment, multi-hop communication, surveillance coverage
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