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Proceedings of the 2021 Sixth International Conference on Research in Intelligent and Computing

Annals of Computer Science and Information Systems, Volume 27

Design of the mobile-robot-based surveillance system on university campuses to reduce the effects of COVID-19 pandemic

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DOI: http://dx.doi.org/10.15439/2021R4

Citation: Proceedings of the 2021 Sixth International Conference on Research in Intelligent and Computing, Vijender Kumar Solanki, Nguyen Ho Quang (eds). ACSIS, Vol. 27, pages 23–28 ()

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Abstract. This paper introduces a new surveillance system to detect wearing-mask and monitor social distancing and body temperature to reduce the effects of the COVID-19 pandemic on university campuses. This surveillance system was designed and implemented to SunBot, an autonomous mobile, based on hardware including Jetson Nano, camera, and thermal camera, and open-source software including OpenCV, YOLOv3, MobilNetv2, TensorFlow, Keras. Both hardware and software are basic, simple to deploy, and affordable cost. Experimental results showed that the surveillance system deployed on university campuses to reduce the effects of the COVID-19 pandemic worked as expected.

References

  1. WHO. Who director-general’s opening remarks at the media briefing on covid-19 - 11 march 2020. [Online]. Available: https://www.who.int/director-general/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-1911-march-2020
  2. L. Meng, F. Hua, and Z. Bian, “Coronavirus disease 2019 (covid-19): emerging and future challenges for dental and oral medicine,” Journal of dental research, vol. 99, no. 5, pp. 481–487, 2020.
  3. Worldometer. Coronavirus update (live): 87,976,426 cases and 1,898,009 deaths from covid-19 virus pandemic - worldometer. [Online]. Available: https://www.worldometers.info/coronavirus/
  4. UNESCO. Coronavirus update (live): 87,976,426 cases and 1,898,009 deaths from covid-19 virus pandemic - worldometer. [Online]. Available: School closures caused by Coronavirus (Covid-19)
  5. M. Loey, G. Manogaran, M. H. N. Taha, and N. E. M. Khalifa, “A hybrid deep transfer learning model with machine learning methods for face mask detection in the era of the covid-19 pandemic,” Measurement, vol. 167, p. 108288, 2021.
  6. ——, “Fighting against covid-19: A novel deep learning model based on yolo-v2 with resnet-50 for medical face mask detection,” Sustainable Cities and Society, vol. 65, p. 102600, 2021.
  7. A. Das, M. W. Ansari, and R. Basak, “Covid-19 face mask detection using tensorflow, keras and opencv,” in 2020 IEEE 17th India Council International Conference (INDICON). IEEE, 2020, pp. 1–5.
  8. N. Petrovic and j. v. p. y. Kocic, DJ, “Iot-based system for covid-19 indoor safety monitoring.”
  9. M. S. Hossain, G. Muhammad, and N. Guizani, “Explainable ai and mass surveillance system-based healthcare framework to combat covid-i9 like pandemics,” IEEE Network, vol. 34, no. 4, pp. 126–132, 2020.
  10. A. J. Sathyamoorthy, U. Patel, Y. A. Savle, M. Paul, and D. Manocha, “Covid-robot: Monitoring social distancing constraints in crowded scenarios,” arXiv preprint https://arxiv.org/abs/2008.06585, 2020.
  11. V. Chamola, V. Hassija, V. Gupta, and M. Guizani, “A comprehensive review of the covid-19 pandemic and the role of iot, drones, ai, blockchain, and 5g in managing its impact,” Ieee access, vol. 8, pp. 90 225–90 265, 2020.
  12. R. R. Murphy, V. B. M. Gandudi, and J. Adams, “Applications of robots for covid-19 response,” arXiv preprint https://arxiv.org/abs/2008.06976, 2020.
  13. D. Feil-Seifer, K. S. Haring, S. Rossi, A. R. Wagner, and T. Williams, “Where to next? the impact of covid-19 on human-robot interaction research,” 2020.
  14. M. Cardona, F. Cortez, A. Palacios, and K. Cerros, “Mobile robots application against covid-19 pandemic,” in 2020 IEEE ANDESCON. IEEE, 2020, pp. 1–5.
  15. M. Tavakoli, J. Carriere, and A. Torabi, “Robotics, smart wearable technologies, and autonomous intelligent systems for healthcare during the covid-19 pandemic: An analysis of the state of the art and future vision,” Advanced Intelligent Systems, vol. 2, no. 7, p. 2000071, 2020.
  16. M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, “Mobilenetv2: Inverted residuals and linear bottlenecks,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 4510–4520.
  17. J. Redmon and A. Farhadi, “Yolov3: An incremental improvement,” arXiv preprint https://arxiv.org/abs/1804.02767, 2018.
  18. ROS-Fundation. Turtlebot2. [Online]. Available: https://www.turtlebot.com/turtlebot2/
  19. M. Bojarski, D. Del Testa, D. Dworakowski, B. Firner, B. Flepp, P. Goyal, L. D. Jackel, M. Monfort, U. Muller, J. Zhang et al., “End to end learning for self-driving cars,” arXiv preprint https://arxiv.org/abs/1604.07316, 2016.
  20. T.-M.-T. Nguyen, T.-H. Diep, B.-N. Bac, N.-B. Le, and X.-Q. Dao, “Design of online learning platform with vietnamese virtual assistant,” in 2021 6th International Conference on Intelligent Information Tech- nology, 2021, pp. 51–57.
  21. X.-Q. Dao, N.-B. Le, and T.-M.-T. Nguyen, “Ai-powered moocs: Video lecture generation,” in 2021 3rd International Conference on Image, Video and Signal Processing, 2021, pp. 95–102.
  22. RasaHQ. Open source machine learning framework to automate text- and voice-based conversations: Nlu, dialogue management, connect to slack, facebook, and more - create chatbots and voice assistants. [Online]. Available: https://github.com/RasaHQ/rasa
  23. K. Przybylek and I. Shkroba, “Crowd counting ́ a la bourdieu: Automated estimation of the number of people,” Computer Science and Information Systems, no. 00, pp. 29–29, 2020.
  24. S. Ansari and S. Salankar, “An overview on thermal image processing.” in RICE, 2017, pp. 117–120.
  25. Chandrikadeb. Face-mask-detection: Face mask detection system based on computer vision and deep learning using opencv and tensor-flow/keras. [Online]. Available: https://github.com/chandrikadeb7/Face-Mask-Detection