FaceEngine: A Tracking-Based Framework for Real-Time Face Recognition in Video Surveillance System-Reference-Cited by-同舟云学术

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FaceEngine: A Tracking-Based Framework for Real-Time Face Recognition in Video Surveillance System

Published:2024-05-31 Issue:5 Volume:5 Page:
ISSN:2661-8907
Container-title:SN Computer Science
language:en
Short-container-title:SN COMPUT. SCI.

Author:

Imran Ahsan,Ahmed Riad,Hasan Md Mehedi,Ahmed M. Helal Uddin,Azad A. K. M.,Alyami Salem A.

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s42979-024-02922-1.pdf

Reference96 articles.

1. Liu Z, Cai Y, Wang H, Chen L, Gao H, Jia Y, Li Y. Robust target recognition and tracking of self-driving cars with radar and camera information fusion under severe weather conditions. IEEE Trans Intell Transp Syst. 2021;23(7):6640–53.

2. Azhar MIH, Zaman FHK, Tahir NM, Hashim H. People tracking system using deepsort. In: 2020 10th IEEE international conference on control system, computing and engineering (ICCSCE). IEEE; 2020. p. 137–41.

3. Zhang X, Hao X, Liu S, Wang J, Xu J, Hu J. Multi-target tracking of surveillance video with differential yolo and deepsort. In: Eleventh international conference on digital image processing (ICDIP 2019), vol. 11179. SPIE; 2019. p. 701–10.

4. Thomas AL, Piza EL, Welsh BC, Farrington DP. The internationalisation of cctv surveillance: effects on crime and implications for emerging technologies. Int J Comp Appl Crim Justice. 2022;46(1):81–102.

5. Costin A. Security of cctv and video surveillance systems: threats, vulnerabilities, attacks, and mitigations. In: Proceedings of the 6th international workshop on trustworthy embedded devices. Vienna: Association for Computing Machinery; 2016. p. 45–54.

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