Skip to main content

Social Distancing Detector Using YOLO3 Algorithm

  • Conference paper
  • First Online:
Advances in Information Communication Technology and Computing

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 628))

  • 242 Accesses

Abstract

In 2019 our world was introduced by a pandemic named as corona which had taken a lot of lives, because of which our India has suffered a lot. So we decided to make something which can help peoples to follow the norms of government and can take care of others as well as themselves. Hence we had decided to work on this project. The social distancing among persons are the best solution for avoiding the COVID and thus this paper is based on similar aspects. The paper proposes a solution for maintaining the distance among person as per guidelines by using YOLO based algorithm and proposed a solution as “Social Distancing Detector”. Social distance detector is used to provide information about people who disobey the norms of maintaining the distance between any two persons and provide demarcation by means of red rectangular box for those who are not maintaining the proper distance among themselves whereas segregating other with green rectangle boxes. Thus, social distancing can be monitored through remote location and having clear demarcation.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
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

Similar content being viewed by others

References

  1. Nepal U, Eslamiat H (2022) Comparing YOLOv3, YOLOv4 and YOLOv5 for autonomous landing spot detection in faulty UAVs. Sensors 22(2):464

    Article  Google Scholar 

  2. Redmon J, Farhadi A (2018) YOLOv3: an incremental improvement, University of Washington. arXiv:1804.02767v1 [cs.CV]

    Google Scholar 

  3. Viraktamath SV, Yavagal M, Byahatti R (2021) Object detection and classification using YOLOv3. Int J Eng Res Technol (IJERT) 10(2):1–6. ISSN: 2278–0181

    Google Scholar 

  4. Huang R, Pedoeem J, Chen C (2018) YOLO-LITE: a real-time object detection algorithm optimized for non-GPU computers. arXiv:1811.05588v1 [cs.CV]

    Google Scholar 

  5. Bochkovskiy A, Wang C-Y, Liao H-YM (2020) YOLOv4: optimal speed and accuracy of object detection. arXiv:2004.10934v1 [cs.CV]

    Google Scholar 

  6. Wang C-Y, Bochkovskiy A, Liao H-YM (2021) Scaled-YOLOv4: scaling cross stage partial network. arXiv:2011.08036v2 [cs.CV]

    Google Scholar 

  7. Zhao Z-Q, Zheng P, Xu S-T, Wu X (2019) Object detection with deep learning: a review. arXiv:1807.05511v2 [cs.CV]

    Google Scholar 

  8. Rajput S, Patni J, Alshamrani S, Chaudhari V, Dumka A, Singh R, Rashid M, Gehlot A, Alghamdi A (2022) Automatic vehicle identification and classification model using the YOLOv3 algorithm for a toll management system. Sustainability 14. https://doi.org/10.3390/su14159163

  9. Aishwarya CN, Mukherjee R, Mahato DK Multilayer vehicle classification integrated with single frame optimized object detection framework using CNN based deep learning architecture. In: 2018 IEEE International conference on electronics, computing and communication technologies (CONECCT), Bangalore, India, pp 1–6. https://doi.org/10.1109/CONECCT.2018.8482366

  10. Song H, Liang H, Li H, Dai Z, Yun X (2019) Vision-based vehicle detection and counting system using deep learning in highway scenes. Eur Transp Res Rev 11:51. https://doi.org/10.1186/s12544-019-0390-4

  11. Martinez-Alpiste I, Golcarenarenji G, Wang Q, Alcaraz C, Jose M (2021) A dynamic discarding technique to increase speed and preserve accuracy for YOLOv3. Neural Comput Appl 33:9961–9973. Springer

    Google Scholar 

  12. Yin X, Sasaki Y, Wang W, Shimizu K (2020) YOLO and K-means based 3D object detection method on image and point cloud. arXiv:2004.11465v1 [cs.CV]

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ankur Dumka .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Dumka, A., Chaudhari, V., Gangotkar, D., Ashok, A., Yadav, D. (2023). Social Distancing Detector Using YOLO3 Algorithm. In: Goar, V., Kuri, M., Kumar, R., Senjyu, T. (eds) Advances in Information Communication Technology and Computing. Lecture Notes in Networks and Systems, vol 628. Springer, Singapore. https://doi.org/10.1007/978-981-19-9888-1_50

Download citation

  • DOI: https://doi.org/10.1007/978-981-19-9888-1_50

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-9887-4

  • Online ISBN: 978-981-19-9888-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics