Skip to main content
SpringerLink
Log in

Leveraging Deep Learning and IoT for Monitoring COVID19 Safety Guidelines Within College Campus

  • Conference paper
  • First Online:
Advanced Computing (IACC 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1367))

Included in the following conference series:

  • 1477 Accesses

Abstract

The widespread coronavirus pandemic 2019 (COVID-19) has brought global emergency with its deadly spread to roundabout 215 countries, and about 4,448,082 Active cases along with 535,098 deaths globally as on July 5, 2020 [1]. The non-availability of any vaccine and low immunity against COVID19 upsurges the exposure of human beings to this virus. In the absence of any vaccine, WHO guidelines like social distancing, wearing masks, washing hands and using sanitizers is the only solution against this pandemic. However, there is no idea when the pandemic situation that the world is going through will come to an end, we can take a breath of relief that someday we will surely go back to our colleges. Although having students wait in line to be screened for COVID19 symptoms may prove logistically challenging. Enthused by this belief, this paper proposes an IoT and deep learning-based framework for automating the task of verifying mask protection and measuring the body temperature of all the students entering the campus. This paper provides a human-less screening solution using a deep learning model to flag no facemasks on students entering the campus and non-contact temperature sensor MLX90614 to detect elevated body temperatures to reduce the risk of exposure to COVID19.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. WHO Homepage. https://www.who.int/health-topics/coronavirus#tab=tab_3. Accessed 16 July 2020

  2. Ourworldindata Homepage. https://ourworldindata.org/. Accessed 14 July 2020

  3. Report WHO-China Joint Mission Coronavirus Disease 2019 (COVID-19), February 2020. https://www.who.int/docs/default-source/coronaviruse/who-china-joint-mi%ssion-on-covid-19-final-report.pdf. Accessed 14 July 2020

  4. Modes of Transmission of Virus Causing COVID-19: Implications for IPC Precaution Recommendations, April 2020. https://www.who.int/news-room/commentaries/detail/modes-of-transmission%-of-virus-causing-covid-19-implications-for-ipc-precaution-recommendations. Accessed 14 July 2020

  5. Study Suggests New Coronavirus May Remain on Surfaces for Days, March 2020. https://www.nih.gov/news-events/nih-research-matters/study-suggests-new%-coronavirus-may-remain-surfaces-days. Accessed 15 July 2020

  6. Coronavirus Disease (COVID-19) Advice for the Public: When and How to Use Masks, April 2020. https://www.who.int/emergencies/diseases/novel-coronavirus-2019/advice-%for-public/when-and-how-to-use-masks. Accessed 15 July 2020

  7. Ting, D.S.W., Carin, L., Dzau, V., Wong, T.Y.: Digital technology and COVID-19. Nat. Med. 26(4), 459–461 (2020)

    Article  Google Scholar 

  8. Digital Technology For Covid-19 Response, April 2020. https://www.who.int/news-room/detail/03-04-2020-digital-technology-for-%covid-19-response. Accessed 16 July 2020

  9. Nguyen-Meidine, L.T., Granger, E., Kiran, M., Blais-Morin, L.: A comparison of CNN-based face and head detectors for real-time video surveillance applications. In: 2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA), Montreal, QC, pp. 1–7 (2017). https://doi.org/10.1109/ipta.2017.8310113

  10. Alabort-i-medina, J., Antonakos, E., Booth, J., Snape, P.: Menpo: a comprehensive platform for parametric image alignment and visual deformable models categories and subject descriptors, pp. 3–6 (2014)

    Google Scholar 

  11. Zhu, X., Ramanan, D.: Face detection, pose estimation, and landmark localization in the wild. In: CVPR (2012)

    Google Scholar 

  12. Morency, L.-P., Whitehill, J., Movellan, J.R.: Generalized adaptive view-based appearance model: integrated frame-work for monocular head pose estimation. In: FG (2008)

    Google Scholar 

  13. Fanelli, G., Gall, J., Gool, L.V.: Real time head pose estimation with random regression forests. In: CVPR, pp. 617–624 (2011)

    Google Scholar 

  14. Asthana, A., Zafeiriou, S., Cheng, S., Pantic, M.: Robust discriminative response map fitting with constrained local models. In: CVPR (2013)

    Google Scholar 

  15. Asthana, A., Zafeiriou, S., Cheng, S. Pantic, M.: Incremental face alignment in the wild. In: CVPR (2014)

    Google Scholar 

  16. Hansen, D.W., Ji, Q.: In the eye of the beholder: a survey of models for eyes and gaze. IEEE Trans. Pattern Anal. Mach. Intell. 32, 478–500 (2010)

    Article  Google Scholar 

  17. Lidegaard, M., Hansen, D.W., Krüger, N.: Head mounted device for point-of-gaze estimation in three dimensions. In: Proceedings of the Symposium on Eye Tracking Research and Applications - ETRA 2014 (2014)

    Google Scholar 

  18. Świrski, L., Bulling, A., Dodgson, N.A.: Robust real-time pupil tracking in highly off-axis images. In: Proceedings of ETRA (2012)

    Google Scholar 

  19. Ferhat, O., Vilarino, F.: A cheap portable eye–tracker solution for common setups. In: 3rd International Workshop on Pervasive Eye Tracking and Mobile Eye-Based Interaction (2013)

    Google Scholar 

  20. Wood, E., Bulling, A.: EyeTab: model-based gaze estimation on unmodified tablet computers. In: Proceedings of ETRA, March 2014

    Google Scholar 

  21. Zielinski, P.: Opengazer: open-source gaze tracker for ordinary webcams (2007)

    Google Scholar 

  22. Deng, J., Dong, W., Socher, R., Li, L., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, pp. 248–255 (2009). https://doi.org/10.1109/cvpr.2009.5206848

  23. Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)

    Google Scholar 

  24. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  25. Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.: MobileNetV2: inverted residuals and linear bottlenecks. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, pp. 4510–4520 (2018). https://doi.org/10.1109/cvpr.2018.00474

  26. Sensor. https://olegkutkov.me/2017/08/10/mlx90614-raspberry/. Accessed 20 Apr 2020

  27. GitHub Repository. https://github.com/waveform80/picamera. Accessed 05 June 2020

  28. Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, Kauai, HI, USA, p. I-I (2001) https://doi.org/10.1109/cvpr.2001.990517

  29. Amos, B., Ludwiczuk, B., Satyanarayanan, M.: OpenFace: a general-purpose face recognition library with mobile applications. CMU-CS-16-118, CMU School of Computer Science, Technical report (2016)

    Google Scholar 

  30. Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, pp. 815–823 (2015). https://doi.org/10.1109/cvpr.2015.7298682

  31. TensorFlow Homepage. https://www.tensorflow.org/. Accessed 19 June 2020

  32. GitHub Repository. https://github.com/iwantooxxoox/Keras-penFace/tree/master/weights. Accessed 16 Apr 2020

  33. Lungu, I.A., Hu, Y., Liu, S.: Multi-resolution siamese networks for one-shot learning. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Genova, Italy, pp. 183–187 (2020). https://doi.org/10.1109/aicas48895.2020.9073996

  34. Bromley, J., et al.: Signature verification using a siamese time delay neural network. Int. J. Pattern Recogn. Artif. Intell. 7(04), 669–688 (1993)

    Article  Google Scholar 

  35. Koch, G.: Siamese neural networks for one-shot image recognition. In: ICML Deep Learning Workshop (2015)

    Google Scholar 

  36. LFW Dataset. http://vis-www.cs.umass.edu/lfw/person/Sylvester_Stallone.html. Accessed 02 May 2020

  37. OpenCV Homepage. https://opencv.org/. Accessed 18 June 2020

  38. Kaggle Datasets. https://www.kaggle.com/datasets. Accessed 28 June 2020

  39. GitHub Repository. https://github.com/X-zhangyang/Real-World-Masked-Face-Dataset. Accessed 29 Apr 2020

  40. Raspberry Pi Products. https://www.raspberrypi.org/products/raspberry-pi-3-model-b-plus/. Accessed 19 Apr 2020

  41. Raspberry Pi Products. https://www.raspberrypi.org/products/camera-module-v2/. Accessed 19 Apr 2020

  42. Sparkfun Sensors Datasheets. https://www.sparkfun.com/datasheets/Sensors/Temperature/MLX90614_rev001.pdf. Accessed 20 Apr 2020

  43. Viola, P., Jones, M.J.: Robust real-time face detection. J. Comput. Vis. 57(2), 137–154 (2004)

    Article  Google Scholar 

  44. Yan, J., Zhang, X., Lei, Z., Li, S.Z.: Real-time high-performance deformable model for face detection in the wild

    Google Scholar 

  45. Liu, W., et al.: SSD: single shot multibox detector. CoRR, abs/1512.02325 (2015)

    Google Scholar 

  46. Ren, S., et al.: Faster R-CNN: towards real-time object detection with region proposal networks. CoRR, abs/1506.01497 (2015)

    Google Scholar 

  47. Dai, J., Li, Y., He, K., Sun, J.: R-FCN: object detection via region-based fully convolutional networks. CoRR, abs/1605.06409 (2016)

    Google Scholar 

  48. Kim, K., Cheon, Y., Hong, S., Roh, B., Park, M.: PVANET: deep but lightweight neural networks for real-time object detection. CoRR, abs/1608.08021 (2016)

    Google Scholar 

  49. Vu, T., Osokin, A., Laptev, I.: Context-aware CNNs for person head detection. In: ICCV (2015)

    Google Scholar 

  50. Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger. CoRR,abs/1612.08242 (2016)

    Google Scholar 

  51. Huang, G.B., Ramesh, M., Berg, T., Learned-Miller, E.: Labeled faces in the wild: a database for studying face recognition in unconstrained environments. Technical Report 07-49, University of Massachusetts, Amherst, October 2007

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Fr. Conceicao Rodrigues College of Engineering, University of Mumbai, Mumbai, Maharashtra, India

    Sahai Vedant, D’Costa Jason, Srivastava Mayank & Mehra Mahendra

  2. Sardar Patel Institute of Technology, University of Mumbai, Mumbai, Maharashtra, India

    Kalbande Dhananjay

Authors
  1. Sahai Vedant
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. D’Costa Jason
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Srivastava Mayank
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mehra Mahendra
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Kalbande Dhananjay
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sahai Vedant .

Editor information

Editors and Affiliations

  1. Bennett University, Greater Noida, Uttar Pradesh, India

    Deepak Garg

  2. University College London, London, UK

    Kit Wong

  3. Missouri University of Science and Technology, Rolla, MO, USA

    Jagannathan Sarangapani

  4. Bennett University, Greater Noida, Uttar Pradesh, India

    Suneet Kumar Gupta

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Vedant, S., Jason, D., Mayank, S., Mahendra, M., Dhananjay, K. (2021). Leveraging Deep Learning and IoT for Monitoring COVID19 Safety Guidelines Within College Campus. In: Garg, D., Wong, K., Sarangapani, J., Gupta, S.K. (eds) Advanced Computing. IACC 2020. Communications in Computer and Information Science, vol 1367. Springer, Singapore. https://doi.org/10.1007/978-981-16-0401-0_3

Download citation

  • DOI: https://doi.org/10.1007/978-981-16-0401-0_3

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-0400-3

  • Online ISBN: 978-981-16-0401-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation