Skip to main content
SpringerLink
Log in

Public Social Distance Monitoring System Using Object Detection YOLO Deep Learning Algorithm

  • Original Research
  • Published:
SN Computer Science Aims and scope Submit manuscript

Abstract

Computer vision and deep learning are emerging technologies as the backbone system to maintain the public healthcare sector to detect the object and surrounding, especially during the COVID-19 pandemic. Generally, in a single stage, you only look once version 3 (YOLOv3) algorithms promising the best results to detect the object in images, live feeds, or videos by learning features at a faster rate than two-stage algorithms such as R-CNN, fast CNN, and faster CNN. Deep sort methods were employed to track identified people by supporting bounding boxes and calculating the Euclidian distances between the people to maintain social distance. Moreover, the YOLOV3 model requires more computational cost to detect the object at best with a lower detection time. Hence, it motivates us to practice a single graphics processing unit (GPU) with the multithreaded approach to increase the frames per second at detection. The proposed model uses a background modeling method grounded on frame variance accumulation which is used to define the number of frames and weight updating. This approach uses two steps, localization of the object and then the classification of localized objects. Distances between people are calculated and compared with threshold values to facilitate comparison. The threshold limit triggers the alert system which is accessible to people, monitoring many video streams at a time. The model is tested based on processors, threads consumed, and various types of inputs ranging from static images to moving videos. Tiny-YOLOv3 performs with the best frames per second and the least processing time, followed by SPP-YOLOv3 and YOLOv3. The model proves its evidence on various parameters and metrics to work robustly. As well as the reason to adopt YOLOv3 over other YOLOv4 and YOLOV5 is tabulated. This model initiates the curiosity to develop a mobile application with security systems based on IoT and CCTV to monitor crowded places.

Graphical Abstract

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26
Fig. 27
Fig. 28
Fig. 29
Fig. 30

Similar content being viewed by others

References

  1. Unhale SS, et al. A review on corona virus (COVID-19). World J Pharm life Sci. 2020;6(4):109–15.

    Google Scholar 

  2. Harapan H, et al. Coronavirus disease 2019 (COVID-19): a literature review. J Infect Public Health. 2020;13(5):667–73.

    Article  Google Scholar 

  3. Liu W, et al. “Ssd: Single shot multibox detector. European conference on computer vision. Cham: Springer International Publishing; 2016. p. 21–37.

    Google Scholar 

  4. Ahmed I, Ahmad A, Piccialli F, Sangaiah AK, Jeon G. A robust features-based person tracker for overhead views in industrial environment. IEEE Internet Things J. 2017;5(3):1598–605.

    Article  Google Scholar 

  5. Ahmed I, Ahmad Mi, Ahmad A, Jeon G. Top view multiple people tracking by detection using deep SORT and YOLOv3 with transfer learning: within 5G infrastructure. Int J Mach Learn Cybern. 2021. https://doi.org/10.1007/s13042-020-01220-5.

    Article  Google Scholar 

  6. Choi J-W, Moon D, Yoo J-H. Robust multi-person tracking for real-time intelligent video surveillance. ETRI J. 2015;37(3):551–61.

    Article  Google Scholar 

  7. Zhao K, Ren X. Small aircraft detection in remote sensing images based on YOLOv3. IOP Conf Ser Mater Sci Eng. 2019;533(1):12056.

    Article  Google Scholar 

  8. Li M, Zhang Z, Lei L, Wang X, Guo X. Agricultural greenhouses detection in high-resolution satellite images based on convolutional neural networks: comparison of faster R-CNN, YOLO v3 and SSD. Sensors. 2020;20(17):4938.

    Article  Google Scholar 

  9. Dorrer MG, Tolmacheva AE. Comparison of the YOLOv3 and Mask R-CNN architectures’ efficiency in the smart refrigerator’s computer vision. J Phys Conf Ser. 2020;1679(4):42022.

    Article  Google Scholar 

  10. Rahim A, Maqbool A, Rana T. Monitoring social distancing under various low light conditions with deep learning and a single motionless time of flight camera. PLoS One. 2021;16(2): e0247440.

    Article  Google Scholar 

  11. Ahamad AH, Zaini N, Latip MFA. “Person detection for social distancing and safety violation alert based on segmented ROI.” In: 2020 10th IEEE international conference on control system, computing and engineering (ICCSCE), Penang, Malaysia, 2020. p. 113–118.

  12. Saponara S, Elhanashi A, Zheng Q. Developing a real-time social distancing detection system based on YOLOv4-tiny and bird-eye view for COVID-19. J Real-Time Image Process. 2022;19(3):551–63.

    Article  Google Scholar 

  13. Ren P, Fang W, Djahel S. “A novel YOLO-Based real-time people counting approach.” In: 2017 international smart cities conference (ISC2), Wuxi, China. 2017. p. 1–2.

  14. Shorfuzzaman M, Hossain MS, Alhamid MF. Towards the sustainable development of smart cities through mass video surveillance: a response to the COVID-19 pandemic. Sustain Cities Soc. 2021;64: 102582.

    Article  Google Scholar 

  15. Ge Z, Liu S, Wang F, Li Z, Sun J. “Yolox: exceeding yolo series in 2021.” arXiv Prepr. arXiv2107.08430. 2021.

  16. Gündüz MS, Işık G. A new YOLO-based method for social distancing from real-time videos. Neural Comput Appl. 2023. https://doi.org/10.1007/s00521-023-08556-3.

    Article  Google Scholar 

  17. Khan A, Sohail A, Zahoora U, Qureshi AS. A survey of the recent architectures of deep convolutional neural networks. Artif Intell Rev. 2020;53(8):5455–516.

    Article  Google Scholar 

  18. Zhao Z-Q, Zheng P, Xu S, Wu X. Object detection with deep learning: a review. IEEE Trans neural networks Learn Syst. 2019;30(11):3212–32.

    Article  Google Scholar 

  19. Zhang G, Wang P, Chen H, Zhang L. Wireless indoor localization using convolutional neural network and Gaussian process regression. Sensors. 2019;19(11):2508.

    Article  Google Scholar 

  20. Shalini GV, Margret MK, Niraimathi MJS, Subashree S. Social distancing analyzer using computer vision and deep learning. J Phys Conf Ser. 2021;1916(1):12039.

    Article  Google Scholar 

  21. Ren S, He K, Girshick R, Sun J. Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Transact Patt Anal Mach Intell. 39(6):1137–1149. https://doi.org/10.1109/TPAMI.2016.2577031.

    Article  Google Scholar 

  22. Mareeswari V, Vijayan R, Sathiyamoorthy E, Ephzibah EP. A narrative review of medical image processing by deep learning models: origin to COVID-19. Int J Adv Technol Eng Explor. 2022;9:623–43.

    Google Scholar 

  23. Nguyen CT, et al. A comprehensive survey of enabling and emerging technologies for social distancing Part I: fundamentals and enabling technologies. Ieee Access. 2020;8:153479–507.

    Article  Google Scholar 

  24. Ansari M, Singh DK. Monitoring social distancing through human detection for preventing/reducing COVID spread. Int J Inf Technol. 2021;13(3):1255–64.

    Google Scholar 

  25. 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 Process. 2021;18(6):1937–47.

    Article  Google Scholar 

  26. Dehghan A, Shah M. Binary quadratic programing for online tracking of hundreds of people in extremely crowded scenes. IEEE Trans Pattern Anal Mach Intell. 2017;40(3):568–81.

    Article  Google Scholar 

  27. Ahmed I, Adnan A. A robust algorithm for detecting people in overhead views. Cluster Comput. 2018;21(1):633–54.

    Article  MathSciNet  Google Scholar 

  28. Srivastava S, Divekar AV, Anilkumar C, Naik I, Kulkarni V, Pattabiraman V. Comparative analysis of deep learning image detection algorithms. J Big Data. 2021;8(1):1–27.

    Article  Google Scholar 

  29. Ahmad T, Ma Y, Yahya M, Ahmad B, Nazir S. Object detection through modified YOLO neural network. Sci Program. 2020;2020:1–10.

    Google Scholar 

  30. Huang Y-Q, Zheng J-C, Sun S-D, Yang C-F, Liu J. Optimized YOLOv3 algorithm and its application in traffic flow detections. Appl Sci. 2020;10(9):3079.

    Article  Google Scholar 

  31. Womg A, Shafiee MJ, Li F, Chwyl B. Tiny SSD: A tiny single-shot detection deep convolutional neural network for real-time embedded object detection. In: 2018 15th Conference on Computer and Robot Vision (CRV). Toronto, ON, Canada, 2018. p. 95–101.

  32. Meivel S, et al. Mask detection and social distance identification using internet of things and faster R-CNN algorithm. Comput Intell Neurosci. 2022;2020:1–13.

    Article  Google Scholar 

  33. Suresh K, Bhuvan S, Palangappa MB. Social distance identification using optimized faster region-based convolutional neural network. In: 2021 5th International Conference on Computing Methodologies and Communication (ICCMC). Erode, India, 2021. p. 753–760.

  34. Khan MZ, Khan MUG, Saba T, Razzak I, Rehman A, Bahaj SA. Hot-Spot zone detection to tackle COVID19 spread by fusing the traditional machine learning and deep learning approaches of computer vision. Ieee Access. 2021;9:100040–9.

    Article  Google Scholar 

  35. Cepni S, Atik ME, Duran Z. Vehicle detection using different deep learning algorithms from image sequence. Balt J Mod Comput. 2020;8(2):347–58.

    Google Scholar 

  36. Huang Z, Wang J, Fu X, Yu T, Guo Y, Wang R. DC-SPP-YOLO: Dense connection and spatial pyramid pooling based YOLO for object detection. Inf Sci (Ny). 2020;522:241–58.

    Article  MathSciNet  Google Scholar 

  37. Zhang X, Gao Y, Wang H, Wang Q. Improve YOLOv3 using dilated spatial pyramid module for multi-scale object detection. Int J Adv Robot Syst. 2020;17(4):1729881420936062.

    Article  Google Scholar 

  38. Zhang X, Wang W, Zhao Y, Xie H. An improved YOLOv3 model based on skipping connections and spatial pyramid pooling. Syst Sci Control Eng. 2021;9(sup1):142–9.

    Article  Google Scholar 

  39. Adarsh P, Rathi P, Kumar M. YOLO v3-tiny: object detection and recognition using one stage improved model. In: 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS). Tamilnadu, India, 2020. p. 687–694.

  40. Fang W, Wang L, Ren P. Tinier-YOLO: a real-time object detection method for constrained environments. IEEE Access. 2019;8:1935–44.

    Article  Google Scholar 

  41. Carrasco DP, Rashwan HA, Puig D. T-YOLO: tiny vehicle detection based on YOLO and multi-scale convolutional neural networks. IEEE Transact Pattern Anal Mach Intell. 2021;39(6):1137–49. https://doi.org/10.1109/TPAMI.2016.2577031

    Article  Google Scholar 

  42. Redmon J, Divvala S, Girshick R, Farhadi A. You only look once: Unified, real-time object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2016. Las Vegas, NV, USA, p. 779–788.

  43. Redmon J, Farhadi A. Yolov3: An incremental improvement. arXiv Prepr. arXiv1804.02767. 2018.

  44. Feng H, Mu G, Zhong S, Zhang P, Yuan T. Benchmark analysis of yolo performance on edge intelligence devices. Cryptography. 2022;6(2):16.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer Science Engineering and Information Systems (SCORE), Vellore Institute of Technology (VIT), Vellore, 632014, India

    Vijayan R & Mareeswari V

  2. Fulton Schools of Engineering, Arizona State University, Tempe, AZ, USA

    Vedant Pople

Authors
  1. Vijayan R
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mareeswari V
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Vedant Pople
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mareeswari V.

Ethics declarations

Conflict of Interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the topical collection “Research Trends in Computational Intelligence” guest edited by Anshul Verma, Pradeepika Verma, Vivek Kumar Singh and S. Karthikeyan.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

R, V., V, M. & Pople, V. Public Social Distance Monitoring System Using Object Detection YOLO Deep Learning Algorithm. SN COMPUT. SCI. 4, 718 (2023). https://doi.org/10.1007/s42979-023-02131-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s42979-023-02131-2

Keywords

Search

Navigation