Open Access
E3S Web Conf.
Volume 351, 2022
10th International Conference on Innovation, Modern Applied Science & Environmental Studies (ICIES’2022)
Article Number 01035
Number of page(s) 4
Published online 24 May 2022
  1. Gralinski, L. E., & Menachery, V. D. Return of the coronavirus: 2019-nCoV. Viruses. 2020; 12: 135. Google Scholar. [Google Scholar]
  2. Prem, K., Liu, Y., Russell, T. W., Kucharski, A. J., Eggo, R. M., Davies, N., … & Klepac, P. (2020). The effect of control strategies to reduce social mixing on outcomes of the COVID-19 epidemic in Wuhan, China: a modelling study. The Lancet Public Health, 5(5), e261–e270. [Google Scholar]
  3. Redmon, Joseph, Divvala, Santosh, Girshick, Ross, et al. You only look once: Unified, real-time object detection. In : Proceedings of the IEEE conference on computer vision and pattern recognition. 2016. p. 779–788. [Google Scholar]
  4. Redmon, Joseph et Farhadi, Ali. Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767, 2018. [Google Scholar]
  5. Bochkovskiy, Alexey, Wang, Chien-Yao, et Liao, Hong-Yuan Mark. Yolov4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934, 2020. [Google Scholar]
  6. Zuo, Fan, Gao, Jingqin, Kurkcu, Abdullah, et al. Reference-free video-to-real distance approximationbased urban social distancing analytics amid COVID-19 pandemic. Journal of Transport & Health, 2021, vol. 21, p. 101032. [CrossRef] [Google Scholar]
  7. Saponara, S., Elhanashi, A. & Gagliardi, A. Implementing a real-time, AI-based, people detection and social distancing measuring system for Covid-19. J Real-Time Image Proc (2021). [Google Scholar]
  8. Ahmed, Imran, Ahmad, Misbah, Rodrigues, Joel JPC, et al. A deep learning-based social distance monitoring framework for COVID-19. Sustainable Cities and Society, 2021, vol. 65, p. 102571. [Google Scholar]
  9. Meivel, S., Devi, K. Indira, Maheswari, S. Uma, et al. Real time data analysis of face mask detection and social distance measurement using Matlab. Materials Today: Proceedings, 2021. [Google Scholar]
  10. S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: towards real-time object detection with region proposal networks,” in Proceedings of the 28th International Conference on Neural Information Processing Systems, Ser. NIPS’15, pp. 91–99, MIT Press, Cambridge, MA, USA, 2015. [Google Scholar]
  11. Qin, Jingchen et Xu, Ning. Reaserch and implementation of social distancing monitoring technology based on SSD. Procedia Computer Science, 2021, vol. 183, p. 768–775. [Google Scholar]
  12. Rahim, A., Maqbool, A., Rana, T. (2021) Monitoring social distancing under various low light conditions with deep learning and a single motionless time of flight camera. [Google Scholar]
  13. Girshick R. Fast r-cnn. In: Proceedings of the IEEE international conference on computer vision; 2015. p. 1440–1448. [Google Scholar]
  14. Girshick, R., Donahue, J., Darrell, T., Malik, J. Region-based convolutional networks for accurate object detection and segmentation. IEEE transactions on pattern analysis and machine intelligence. 2015;38(1):142–158. [Google Scholar]
  15. Joseph Redmon, “Darknet: Open Source Neural Networks in C.” (2013-2016). [Google Scholar]
  16. Redmon, Joseph, and Ali, Farhadi. “YOLO9000: Better, Faster, Stronger” arXiv preprint arXiv:1612.08242 (2016). [Google Scholar]
  17. Barekatain, M., Miquel Marti, Hsueh-Fu Shih, Samuel Murray, K. Nakayama, Y. Matsuo and H. Prendinger. “Okutama-Action: An Aerial View Video Dataset for Concurrent Human Action Detection.” 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (2017): 2153–2160. [CrossRef] [Google Scholar]
  18. Du, Dawei, et al. “VisDrone-DET2019: The Vision Meets Drone Object Detection in Image Challenge Results.” 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW) (2019): 213–226. [CrossRef] [Google Scholar]
  19. Google colab,, last accessed 2021/07/16 [Google Scholar]
  20. Lin, Tsung-Yi, M. Maire, Serge J. Belongie, James Hays, P. Perona, D. Ramanan, Piotr Dollar and C. L. Zitnick. “Microsoft COCO: Common Objects in Context.” ECCV (2014). [Google Scholar]

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