A Deep-Learning Based Automated COVID-19 Physical Distance Measurement System Using Surveillance Video-Reference-Cited by-同舟云学术

A Deep-Learning Based Automated COVID-19 Physical Distance Measurement System Using Surveillance Video

Published:2022 Issue: Volume: Page:210-222
ISSN:1865-0929
Container-title:Communications in Computer and Information Science
language:
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Author:

Junayed Masum Shah^ORCID,Islam Md Baharul^ORCID

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-07005-1_19

Reference32 articles.

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3. Bentafat, E., Rathore, M.M., Bakiras, S.: A practical system for privacy-preserving video surveillance. In: Conti, M., Zhou, J., Casalicchio, E., Spognardi, A. (eds.) Applied Cryptography and Network Security, ACNS 2020. LNCS, vol. 12147, pp. 21–39. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-57878-7_2

4. Bochkovskiy, A., Wang, C.Y., Liao, H.Y.M.: YOLOv4: optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934 (2020)

5. Bouhlel, F., Mliki, H., Hammami, M.: Crowd behavior analysis based on convolutional neural network: social distancing control COVID-19. In: VISIGRAPP (5: VISAPP), pp. 273–280 (2021)

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