• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology


Indian Journal of Science and Technology

Year: 2022, Volume: 15, Issue: 47, Pages: 2628-2638

Original Article

A Conceptual Real-Time Deep Learning Approach for Object Detection, Tracking and Monitoring Social Distance using Yolov5

Received Date:16 September 2022, Accepted Date:10 November 2022, Published Date:21 December 2022


Objectives: To develop a computer vision-based model that can detect, track and recognize individuals for the purpose of measuring social distance in road traffic videos using surveillance cameras. Methods: The real-time traffic surveillance webcam dataset was applied to validate the model, with better performance metrics outperformed to those of comparable cuttingedge models such as RetinaNet, Faster R-CNN and SSD. Our proposed methodology utilized object detection methods to recognize individuals followed by multiple objects tracking approach to track identified individuals using detected bounding boxes. Our research shows that the conventional method is successful in detecting persons who violate social distances. Findings: Our finding shows that our proposed object detection model successfully recognizes human and those who violating the social distancing measurements. For the purpose of detecting social distance, develop a highly accurate detection technique. Our YOLOv5 with multiple objects tracking algorithm delivered great outcomes with appropriate Precision of 93%, Recall of 94%, F1-Score of 96% and mAP of 95% measures given by object categorization and localization to measure social distancing in real-time traffic surveillance videos. The YOLOv5 model’s results are then compared to many other prominent state-of-the-art models. Novelty: The YOLOv5 and MOTSORT is appropriate for finding whether people are maintaining social distancing or not, intended to identify, monitor and track those who are not following or violating social distances with overall accuracy and efficiency. Keywords: Object Detection; Deep Learning; Social distance monitoring; multiple objects tracking; YOLOv5


  1. Sharma T, Debaque B, Duclos N, Chehri A, Kinder B, Fortier P. Deep Learning-Based Object Detection and Scene Perception under Bad Weather Conditions. Electronics. 11(4):563. Available from: https://doi.org/10.3390/electronics11040563
  2. Walia IS, Kumar D, Sharma K, Hemanth JD, Popescu DE. An Integrated Approach for Monitoring Social Distancing and Face Mask Detection Using Stacked ResNet-50 and YOLOv5. Electronics. 10(23):2996. Available from: https://doi.org/10.3390/electronics10232996
  3. Ottakath N, Elharrouss O, Almaadeed N, Al-Maadeed S, Mohamed A, Khattab T, et al. ViDMASK dataset for face mask detection with social distance measurement. Displays. 2022;73:102235. Available from: https://doi.org/10.1016/j.displa.2022.102235
  4. Saeed HH, Alazzawi A. Social distance in object detection: Survey based on cutting-edge deep learning approach. International Journal of Nonlinear Analysis and Applications. 2022;13:2865–2880. Available from: https://doi.org/10.22075/IJNAA.2022.27431.3598
  5. 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. Journal of Real-Time Image Processing. 2022;19(3):551–563. Available from: https://doi.org/10.1007/s11554-022-01203-5
  6. Ilyas N, Ahmad Z, Lee B, Kim K. An effective modular approach for crowd counting in an image using convolutional neural networks. Scientific Reports. 2022;12(1). Available from: https://doi.org/10.1038/s41598-022-09685-w
  7. Yadav S, Gulia P, Gill NS, Chatterjee JM. A Real-Time Crowd Monitoring and Management System for Social Distance Classification and Healthcare Using Deep Learning. Journal of Healthcare Engineering. 2022;2022:1–11. Available from: https://doi.org/10.1155/2022/2130172
  8. Sriharsha M, Jindam S, Gandla A, Allani LS. Social Distancing Detector using Deep Learning. International Journal of Recent Technology and Engineering (IJRTE). 10(5):146–149. Available from: https://doi.org/10.35940/ijrte.E6710.0110522
  9. Zoph B, Cubuk ED, Ghiasi G, Lin TY, Shlens J, Le QV. Learning Data Augmentation Strategies for Object Detection. Computer Vision – ECCV 2020. 2020;p. 566–583. Available from: https://doi.org/10.48550/arXiv.1906.11172
  10. Yurtsever YD, Renganathan E, Redmill V, Ozgüner KA, U. A Vision-Based Social Distancing and Critical Density Detection System for COVID-19. Sensors. 2021;21(13):4608. Available from: https://doi.org/10.3390/s21134608
  11. Aghaei M, Bustreo M, Wang Y, Bailo G, Morerio P, Bue AD. Single Image Human Proxemics Estimation for Visual Social Distancing. In: IEEE Winter Conference on Applications of Computer Vision (WACV). (pp. 2784-2794) IEEE. 2021.


© 2022 Bharathi & Anandharaj.  This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)


Subscribe now for latest articles and news.