Smart System for Crowd Monitoring and Detection to Prevent Covid19

Authors

  • Prof. Gaurav Tiwari  Professor, Department of Electronics and Telecommunication Engineering, D.Y.P.S.O.E, Pune, Maharashtra, India
  • Ajay Darakhe  Student, Department of Electronics and Telecommunication Engineering, D.Y.P.S.O.E, Pune, Maharashtra, India
  • Nikhil Chaudhari  Student, Department of Electronics and Telecommunication Engineering, D.Y.P.S.O.E, Pune, Maharashtra, India
  • Omkar More  Student, Department of Electronics and Telecommunication Engineering, D.Y.P.S.O.E, Pune, Maharashtra, India

DOI:

https://doi.org//10.32628/IJSRSET218369

Keywords:

RaspberryPi4, RPi camera module, Python, OpenCV, Haar cascade, Raspbian.

Abstract

It is essential to maintain social distance and avoid large mob gatherings at one place to break the chain of corona virus infection, but maintaining these things is not that much easy. People knowingly or unknowingly, gather, roam on the streets & break the rules. Hence Keeping an eye on all these activities is not an easy job. The proposed system is an automatic method for controlling crowd in this pandemic situation, where crowd gatherings should be avoided on large basis. We have proposed a system which will keep a watchful eye on crowd gathering with help of RPi camera, as crowd is detected the system will give a alert to authorities that they will take actions against the crowd gatherings and restrict the public from areas where crowds are restricted to be gather. The main aim of the survey is to be found how to avoid unnecessary gatherings where crowd is restricted! and if crowd gathers unnecessarily, system can alert the respective authorities and crowd can be minimized.

References

  1. Rapid Object Detection using a Boosted Cascade of Simple Features, paul viola, Michael jeffery jones, FEB2001 Proceedings / CVPR, IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
  2. Human face detection algorithm via Haar cascade classifier combined with three additional classifiers Li Cuimei, Qi Zhiliang, Jia Nan, Wu Jianhua 2017 13th IEEE International Conference on Electronic Measurement & Instruments (ICEMI), 483-487, 2017
  3. Face Detection and Recognition Using OpenCV Maliha Khan, Sudeshna Chakraborty, Rani Astya, Shaveta Khepra 2019 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), 116-119, 2019
  4. Hybrid approach of haar cascade classifiers and geometrical properties of facial features applied to illumination invariant gender classification system Priyanka Goel, Suneeta Agarwal 2012 International Conference on Computing Sciences, 132-136, 2012
  5. " Multi-faces recognition process using Haar cascades and eigenface methods Teddy Mantoro, Media A Ayu 018 6th International Conference on Multimedia Computing and Systems (ICMCS), 1-5, 2018
  6. Comparison of Viola-Jones Haar Cascade classifier and histogram of oriented gradients (HOG) for face detection C Rahmad, RA Asmara, DRH Putra, I Dharma, H Darmono, I Muhiqqin IOP conference series: materials science and engineering 732 (1), 012038, 2020
  7. The architecture and performance of the face and eyes detection system based on the Haar cascade classifiers Andrzej Kasinski, Adam Schmidt Pattern Analysis and Applications 13 (2), 197-211, 2010
  8. Face detection based on multi-block lbp representation Lun Zhang, Rufeng Chu, Shiming Xiang, Shengcai Liao, Stan Z Li International conference on biometrics, 11-18, 2007
  9. Fast asymmetric learning for cascade face detection Jianxin Wu, S Charles Brubaker, Matthew D Mullin, James M Rehg IEEE transactions on pattern analysis and machine intelligence 30 (3), 369-382, 2008
  10. LIVE FACEMASK DETECTION SYSTEM Harsh Nagoriya, Mohak Parekh, researchgate.net 2020.
  11. Object Detection and Tracking Turret based on Cascade Classifiers and Single Shot Detectors Pritom Gogoi; Manpa Barman; Mahendra Deka; Upasana Rajkonwar; Rhittwikraj Moudgollya, IEEE Published in: 2020 International Conference on Computational Performance Evaluation (ComPE).
  12. Motion-based machine vision techniques for the management of large crowds B.A. Boghossian; S.A. Velastin, IEEE Published in: ICECS'99. Proceedings of ICECS '99. 6th IEEE International Conference on Electronics, Circuits and Systems (Cat. No.99EX357)Year 2002.
  13. Single-Image Crowd Counting via Multi-Column Convolutional Neural Network Yingying Zhang, Desen Zhou, Siqin Chen, Shenghua Gao, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
  14. COVID-Robot: Monitoring Social Distancing Constraints in Crowded Scenarios, Adarsh Jagan Sathyamoorthy, Utsav Patel, Yash Ajay Savle, Moumita Paul, Dinesh Manocha, Publication:eprint arXiv:2008.06585Pub Date:August 2020.
  15. Monitoring Social Distancing for Covid-19 Using OpenCV and Deep Learning Rucha Visal, Atharva Theurkar, Bhairavi Shukla, International Research Journal of Engineering and Technology (IRJET) Volume: 07 Issue: 06 | June 2020.
  16. Autonomous Social Distancing in Urban Environments using a Quadruped Robot Tingxiang Fan, Zhiming Chen, Xuan Zhao, Jing Liang, Cong Shen, Dinesh Manocha, Jia Pan, Wei Zhang, arxiv.org august 2020.
  17. A Vision-based Social Distancing and Critical Density Detection System for COVID-19 Dongfang Yang∗ Ekim Yurtsever∗ Vishnu Renganathan Keith A. Redmill Umit ¨ Ozg ¨ uner, arxiv.org, jul2020.
  18. Human detector and counter using raspberry Pi microcontroller Shubham Mathur; Balaji Subramanian; Sanyam Jain; Kajal Choudhary; D Rama Prabha, 2017 Innovations in Power and Advanced Computing Technologies (i-PACT).
  19. Real-time Violence Detection in Surveillance Videos using RPiMuhammad Salman Hikmat Yar, Tauseef Jan, Khalil Ur Rahman, The 5th International Conference on Next Generation Computing 2019.
  20. Counting and tracking people in a smart room: An IoT approach Dimitris Sgouropoulos; Evaggelos Spyrou; Giorgos Siantikos; Theodoros Giannakopoulos, 2015 10th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP).

International Journal of Scientific Research in Science, Engineering and Technology

Downloads

Published

2021-06-30

Issue

Volume 8, Issue 3, May-June-2021

Section

Research Articles

License

Copyright (c) IJSRSET

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Prof. Gaurav Tiwari, Ajay Darakhe, Nikhil Chaudhari, Omkar More, " Smart System for Crowd Monitoring and Detection to Prevent Covid19, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 8, Issue 3, pp.355-359, May-June-2021. Available at doi : https://doi.org/10.32628/IJSRSET218369