Social Distancing Monitoring System using Deep Learning

Amelia Ritahani Ismail; Nur Shairah Muhd Affendy; Ahsiah Ismail; Asmarani Ahmad Puzi

doi:10.17977/um018v5i12022p17-26

Social Distancing Monitoring System using Deep Learning

Amelia Ritahani Ismail, Nur Shairah Muhd Affendy, Ahsiah Ismail, Asmarani Ahmad Puzi

Abstract

COVID-19 has been declared a pandemic in the world by 2020. One way to prevent COVID-19 disease, as the World Health Organization (WHO) suggests, is to keep a distance from other people. It is advised to stay at least 1 meter away from others, even if they do not appear to be sick. The reason is that people can also be the virus carrier without having any symptoms. Thus, many countries have enforced the rules of social distancing in their Standard Operating Procedure (SOP) to prevent the virus spread. Monitoring the social distance is challenging as this requires authorities to carefully observe the social distancing of every single person in a surrounding, especially in crowded places. Real-time object detection can be proposed to improve the efficiency in monitoring the social distance SOP inspection. Therefore, in this paper, object detection using a deep neural network is proposed to help the authorities monitor social distancing even in crowded places. The proposed system uses the You Only Look Once (YOLO) v4 object detection models for the detection. The proposed system is tested on the MS COCO image dataset with a total of 330,000 images. The performance of mean average precision (mAP) accuracy and frame per second (FPS) of the proposed object detection is compared with Faster Region-based Convolutional Neural Network (R-CNN) and Multibox Single Shot Detector (SSD) model. Finally, the result is analyzed among all the models.

Full Text:

PDF

References

World Health Organization: Coronavirus disease (COVID-19): How is it transmitted? https://www.who.int/news-room/q-a-detail/coronavirus-disease-covid-19-how-is-it-transmitted (2020). Accessed 5 Apr 2021.

I. Leong, PKP: Kadar pematuhan 92 peratus tapi penjarakan sosial gagal dipatuhi - Ismail Sabri. Astro Awani. https://www.astroawani.com/berita-malaysia/pkp-kadar-pematuhan-92-peratus-tapi-penjarakan-sosial-gagal-dipatuhi-ismail-sabri-234881 (2020). Accessed 5 Nov 2020.

A. Povera: Significant increase in the number of SOP flouters. New Straits Times. https://www.nst.com.my/news/nation/2020/07/611744/significant-increase-number-sop-flouters (2020). Accessed 5 Nov 2020.

I. Hilmy: Eight slapped with RM1k compound each for breaching recovery MCO in Penang. The Star. https://www.thestar.com.my/news/nation/2020/07/25/eight-slapped-with-rm1k-compound-each-for-breaching-recovery-mco-in-penang (2020). Accessed 5 Nov 2020.

W. Lan, J. Dang, Y. Wang and S. Wang, "Pedestrian Detection Based on YOLO Network Model," 2018 IEEE International Conference on Mechatronics and Automation (ICMA), 2018, pp. 1547-1551.

N. S. Punn, S. K. Sonbhadra, S. Agarwal, and G. Rai, “Monitoring COVID-19 social distancing with person detection and tracking via fine-tuned YOLO v3 and Deepsort techniques,” May 2020.

D. Yang, E. Yurtsever, V. Renganathan, K. A. Redmill, and Ü. Özgüner, “A Vision-based Social Distancing and Critical Density Detection System for COVID-19,” Jul. 2020.

T.Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C.L. Zitnick. Microsoft COCO: common objects in context. In: Fleet D., Pajdla T., Schiele B., Tuytelaars T. (eds) Computer Vision – ECCV 2014, Lecture Notes in Computer Science, vol. 8693, pp. 740–755. Springer, Cham (2014).

R. Girshick, J. Donahue, T. Darrell and J. Malik, "Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation," 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014, pp. 580-587.

R. Girshick, "Fast R-CNN," 2015 IEEE International Conference on Computer Vision (ICCV), 2015, pp. 1440-1448.

S. Ren, K. He, R. Girshick and J. Sun, "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 6, pp. 1137-1149, 1 June 2017.

W. Liu et al., “SSD: Single Shot MultiBox Detector,” 2016, pp. 21–37.

J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You Only Look Once: Unified, Real-Time Object Detection,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 2016, pp. 779–788.

J. Redmon and A. Farhadi, "YOLO9000: Better, Faster, Stronger," 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 6517-6525.

J. Redmon and A. Farhadi, “YOLOv3: An Incremental Improvement,” Apr. 2018.

A. Bochkovskiy, C.-Y. Wang, and H.-Y. M. Liao, “YOLOv4: Optimal Speed and Accuracy of Object Detection,” Apr. 2020.

C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens and Z. Wojna, "Rethinking the Inception Architecture for Computer Vision," 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 2818-2826.

A. G. Howard et al., “MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications,” Apr. 2017.

C. -Y. Wang, H. -Y. Mark Liao, Y. -H. Wu, P. -Y. Chen, J. -W. Hsieh and I. -H. Yeh, "CSPNet: A New Backbone that can Enhance Learning Capability of CNN," 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2020, pp. 1571-1580.

K. E. Koech: Confusion matrix for object detection. Towards Data Science. https://towardsdatascience.com/confusion-matrix-and-object-detection-f0cbcb634157 (2020). Accessed 29 Apr 2021.

N.-D. Nguyen, T. Do, T. D. Ngo, and D.-D. Le, “An Evaluation of Deep Learning Methods for Small Object Detection,” J. Electr. Comput. Eng., vol. 2020, pp. 1–18, Apr. 2020.

B. Benfold and I. Reid, "Stable multi-target tracking in real-time surveillance video," CVPR 2011, 2011, pp. 3457-3464.

DOI: http://dx.doi.org/10.17977/um018v5i12022p17-26