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Federated Edge for Tracking Mobile Targets on Video Surveillance Streams in Smart Cities
Abstract
Nowadays, video surveillance is a very common practice in Smart Cities. There are public and private video surveillance systems, and very often different systems or single devices frame the same area. However, when a target needs to be identified or needs to be tracked in real-time, such solutions typically require human intervention to configure the devices in the best possible way (e.g., choosing the optimal cameras, setting up their focus, and so on). To address such a problem, in this paper, we define a new interrogation method based on a Federated Edge approach. This approach addresses the problem from the point of view of both camera hardware and shooting angle associated with it. According to the presented approach, it is possible to understand which the best camera to identify a target and possibly tracking it in a specific area is. A case study is defined in the context of urban mobility management.
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In [74], federated edges allow multiple cameras to work together to detect objects from various angles, and selecting the most appropriate camera and prioritizing the highest-rated video feeds saves time and resources and improves efficiency.
“Federated edges in smart city surveillance allow multiple cameras to work together to detect objects from various angles.”
From A Survey of Video Surveillance Systems in Smart City (2023) · cites this paper as [74]
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References 16 · Cited by 4
Research works printed but not linked: 2
- Mirmehdi, 1997, Towards optimal zoom for automatic target recognition
- Kim, Object recognition using multi-ple views, 2, 28
Metadata
- Authors
- Martella, F., Fazio, M., Celesti, A., Lukaj, V., Quattrocchi, A., Di Gangi, M., & Villari, M.
- Affiliations
- University of Messina (IT)
- University of Messina,Italy
- Venue
- 2022 IEEE Symposium on Computers and Communications (ISCC)
- Publisher
- IEEE
- Published
- 2022-06-30
- Volume
- 2
- Pages
- 1-6
- ISSN
- 1530-1346 (ISSN-L)
- OpenAlex
- W4313061676
- Cited by
- 4
- References
- 16
- Citations by year
- 2026: 1 · 2023: 3
- FWCI
- 0.27
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Symposium on Computers and Communications (ISCC) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Keywords
- mobile target tracking, video surveillance, smart cities, camera selection, shooting angle, real-time object tracking, urban mobility management
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