Enhancing the accuracy of target detection in remote video surveillance analytics through federated learning-Reference-Cited by-同舟云学术

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Enhancing the accuracy of target detection in remote video surveillance analytics through federated learning

Published:2023-12-14 Issue:2 Volume:56 Page:
ISSN:0306-8919
Container-title:Optical and Quantum Electronics
language:en
Short-container-title:Opt Quant Electron

Author:

Selvi S.,Aggarwal Kapil,Pandurangan Raji,Vijayan Vinodh P.,Ali Anooja,Anuradha K.

Publisher

Springer Science and Business Media LLC

Subject

Electrical and Electronic Engineering,Atomic and Molecular Physics, and Optics,Electronic, Optical and Magnetic Materials

Link

https://link.springer.com/content/pdf/10.1007/s11082-023-05664-1.pdf

Reference17 articles.

1. Bewley, A., Ge, Z., Ott, L., Ramos, F., Upcroft, B.: Simple online and realtime tracking. In: 2016 IEEE International Conference on Image Processing (ICIP), pp. 3464–3468. IEEE (2016)

2. Chen et al.: Distributed deep learning model for intelligent video surveillance systems with edge computing. IEEE Trans. Indus. Informat. (2020). https://doi.org/10.1109/tii.2019.2909473

3. Conche., Greten Xiao et al.: Towards real-time service from remote sensing: Compression of earth observatory video data via long-term background referencing. Remote Sens. (2018). https://doi.org/10.3390/rs10060876

4. Gao, Y., Zhang, J., Liu, Y.: A survey of video-based human activity recognition. Multimedia Tools Appl. 78(9), 11407–11433 (2019). https://doi.org/10.1007/s11042-019-08170-4

5. Jiang et al.: Federated dynamic graph neural networks with secure aggregation for video-based distributed surveillance. ACM Trans. Intell. Syst. Technol. (2022). https://doi.org/10.1145/3501808

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