Deep Learning Based Real-Time COVID Norms Violation Detection System

Authors

Keywords:

COVID-19, CNN, YOLOv4, Social Distance Analyzer (SDA), Height centroid index

Abstract

Corona virus disease-2019 (COVID-2019) has impacted on many social behaviours and has put forth some cautiousness in day-to-today life. Therefore, to remove the barrier of fearful life, it is essential to monitor the preventive guidelines suggested by the world health organization. The very first guideline to be followed is to wear a mask and maintain social distance. In order to implement this in a super populous country like India, the administration used very coercive steps. To aid the administration, this paper provides a simple and easy to implement deep learning technique for the detection and recognition of COVID norm violators. Given an unconstrained/ constrained real-time video, the proposed framework uses YOLOv4 model for person localization, height-width comparison for evaluating social distance, and a customized YOLOv4 model for face mask detection. Once the proposed algorithm localizes the violators, it identifies them using convolutional neural network-based face recognition library. The evaluation metrics on benchmark datasets as well as real-time data are obtained. The proposed framework outperforms existing solutions with mAP (mAP @ 0.50 i.e. Mean Average Precision) of 0.9395 on YOLOv4. Comparison of proposed technique with the existing literature illustrates the better trade-off between accuracy and complexity.

Downloads

Download data is not yet available.

References

D. Yang, E. Yurtsever, V. Renganathan, K. A. Redmill, and U.Ozguner, “A vision-based social distancing and critical density detection system for covid19,” Sensors, vol. 21, no. 13, pp. 4608, 2021.

J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You look only once: unified real-time object detection,” arXiv preprint arXiv:1506.02640, 2015.

W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg, “Ssd: Single shot multibox detector,” in European conference on computer vision. Springer, pp. 21–37, 2016.

S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks,” Advances in neural information processing systems, vol. 28, pp. 91–99, 2015.

S. Gupta, A. K. Sahoo, and U. K. Sahoo, “Wireless sensor network-based distributed approach to identify spatio-temporal volterra model for industrial distributed parameter systems,” IEEE Transactions on Industrial Informatics, vol. 16, no. 12, pp. 7671–7681, 2020.

Sasithradevi and S. M. M. Roomi, “Video classification and retrieval through spatio-temporal radon features,” Pattern recognition, vol. 99, pp. 107099, 2020.

R. Kumar, A. Kumar, and G. K. Singh, “Electrocardiogram signal compression using singular coefficient truncation and wavelet coefficient coding,” IET Science, Measurement & Technology, vol. 10, no. 4, pp. 266–274, 2016.

A. Loukkal, Y. Grandvalet, T. Drummond, and Y. Li, “Driving among flatmobiles: Bird-eye-view occupancy grids from a monocular camera for holistic trajectory planning,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 51–60, 2021.

Xu, Jiaojiao and Gong, Chen and Xu, Zhengyuan, “Experimental indoor visible light positioning systems with centimeter accuracy based on a commercial smartphone camera”, IEEE Photonics Journal, Vol.10, No.6, pp.1-17, 2018.

S. Ge, J. Li, Q. Ye, and Z. Luo, “Detecting masked faces in the wild with lle-cnns,” in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2682–2690, 2017.

Y. Chen, L. Song, Y. Hu, and R. He, “Adversarial occlusion-aware face detection,” in IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS). IEEE, pp. 1–9, 2018.

Alonso-Fernandez, Fernando and Diaz, Kevin Hernandez and Ramis, Silvia and Perales, Francisco J and Bigun, Josef, “Soft-biometrics estimation in the era of facial masks”, 2020 International Conference of the Biometrics Special Interest Group (BIOSIG), pp.1-6, 2020.

F. Schroff, D. Kalenichenko, and J. Philbin, “Facenet: A unified embedding for face recognition and clustering,” in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 815–823, 2015.

L. Blanger and A. Panisson, “A face recognition library using convolutional neural networks,” International Journal of Engineering Research and Science, vol. 3, no. 8, pp. 84–92, 2017.

P. Khandelwal, A. Khandelwal, S. Agarwal, D. Thomas, N. Xavier, and A. Raghuraman, “Using computer vision to enhance safety of workforce in manufacturing in a post covid world,” arXiv preprint arXiv:2005.05287, 2020.

A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam, “Mobilenets: Efficient convolutional neural networks for mobile vision applications,” arXiv preprint arXiv:1704.04861, 2017.

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,” arXiv preprint arXiv:2005.01385, 2020.

O. Arandjelovi´c, D.-S. Pham, and S. Venkatesh, “Cctv scene perspective distortion estimation from low-level motion features,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 26, no. 5, pp. 939–949, 2015.

D. T. Nguyen, T. N. Nguyen, H. Kim, and H.-J. Lee, “A high-throughput and power-efficient fpga implementation of yolo cnn for object detection,” IEEE Transactions on Very Large-Scale Integration (VLSI) Systems, vol. 27, no. 8, pp. 1861–1873, 2019.

Velazquez-Pupo, R.; Sierra-Romero, A.; Torres-Roman, D.; Shkvarko, Y.V.; Santiago-Paz, J.; Gómez-Gutiérrez, D.; Robles-Valdez, D.; Hermosillo-Reynoso, F.; Romero-Delgado, M. “Vehicle Detection with Occlusion Handling, Tracking, and OC-SVM Classification: A High-Performance Vision-Based System”. Sensors 2018, 18, 374. https://doi.org/10.3390/s18020374

Koklu M, Cinar I, Taspinar YS. “CNN-based bi-directional and directional long-short term memory network for determination of face mask”, Biomed Signal Process Control, Vol. 71, pp. 103216, 2022.

Cabani A, Hammoudi K, Benhabiles H, Melkemi M., “MaskedFace-Net - A dataset of correctly/incorrectly masked face images in the context of COVID-19”, Smart Health, Vol.19, pp.100144,2021.

https://cocodataset.org/, last access: 4.01.2022

https://www.kaggle.com/rahulmangalampalli/mafa-data, last access: 4.01.2022

http://vision.ucsd.edu/content/yale-face-database, last access: 4.01.2022

Downloads

Published

27.05.2022

How to Cite

[1]
S. Anbalagan, S. . Gupta, N. P, and M. Roomi S, “Deep Learning Based Real-Time COVID Norms Violation Detection System”, Int J Intell Syst Appl Eng, vol. 10, no. 2, pp. 175–180, May 2022.

Issue

Section

Research Article