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Crowd Monitoring Using Machine Learning

Arivoli K.*, Daniel Andrew B.**, Fraser Kagoo E. ***, Harshavardhan S. A. ****, Tamilarasi M. *****

*-***** Department of Electronics and Communication Engineering, Rajalakshmi Engineering College, Chennai, Tamil Nadu, India.

Periodicity:January - June'2021
DOI : https://doi.org/10.26634/jdp.9.1.18227

World Health Organization : COVID-19 - Global literature on coronavirus disease

https://pesquisa.bvsalud.org/global-literature-on-novel-coronavirus-2019-ncov/resource/en/covidwho-1525391

ProQuest Central | ID: covidwho-1525391

Abstract

Crowd detection and density estimation from crowded images have a wide range of application such as crime detection, congestion, public safety, crowd abnormalities, visual surveillance and urban planning. The purpose of crowd density analysis is to calculate the concentration of the crowd in the videos of observers. The job of detecting a face in the crowd is complicated due to the variability present in human faces including color, pose, expression, position, orientation, and illumination. This paper proposes a deep learning based framework for automating the task of monitoring social distancing using surveillance video.

Keywords

Crowd Detection, Social Distance, Deep Learning.

How to Cite this Article?

Arivoli, K., Andrew, B. D., Kagoo, E. F., Harshavardhan, S. A., and Tamilarasi, M. (2021). Crowd Monitoring Using Machine Learning. i-manager's Journal on Digital Signal Processing, 9(1), 35-38. https://doi.org/10.26634/jdp.9.1.18227

References

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[2]. Chebi, H., Tabet-Derraz, H., Sayah, R., Benaissa, A., Meroufel, A., Acheli, D., & Meraihi, Y. (2020). Intelligence and adaptive global algorithm detection of crowd behavior. International Journal of Computer Vision and Image Processing (IJCVIP), 10(1), 24-41. https://doi.org/ 10.4018/IJCVIP.2020010102

[3]. Khan, K., Albattah, W., Khan, R. U., Qamar, A. M., & Nayab, D. (2020). Advances and trends in real time visual crowd analysis. Sensors, 20(18), 5073. https://doi.org/10. 3390/s20185073

[4]. Shalash, W. M., AlZahrani, A. A., & Al-Nufaii, S. H. (2019, nd May). Crowd Detection Management System. In 2019, 2 International Conference on Computer Applications & Information Security (ICCAIS) (pp. 1-8). IEEE. ttps://doi.org/ 10.1109/CAIS.2019.8769566

[5]. Sikdar, A., & Chowdhury, A. S. (2020). Scale-invariant batch-adaptive residual learning for person reidentification. Pattern Recognition Letters, 129, 279-286. https://doi.org/10.1016/j.patrec.2019.11.032

[6]. Tzelepi, M., & Tefas, A. (2017, August). Human crowd detection for drone flight safety using convolutional neural networks. In 2017, 25th European Signal Processing Conference (EUSIPCO) (pp. 743-747). IEEE. https://doi.org/ 10.23919/EUSIPCO.2017.8081306

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