Automated Inspection of Face Mask and Social Distancing Using Artificial Intelligence
Abstract
According to the data
obtained by the World Health Organization, COVID-19, and the global pandemic
has severely impacted the world and infected more than hundreds of million
people worldwide which includes more than three million deaths. This global
pandemic enforced governments across the world to impose lockdowns in order to
prevent the virus transmissions. Reports indicate that wearing face masks and
following safe social distancing are two of the enhanced safety protocols need
to be followed in public places in order to prevent the spread of the virus. To
ensure the public safety in environment, we propose an efficient Computer
Vision based approach that focused on the real-time automated monitoring of
people to detect both safe social distancing and face masks in public places by
implementing the model to monitor the activity and detect violations through
camera. After detection of breach, the system sends to control center at state
police headquarters and also give alarm to public. In this proposed system,
modern deep neural network based model have been mixed with geometric
techniques for building a robust model which covers three aspects of detection,
tracking, and validation. Thus, the proposed system helps the society in saving
time and reducing the spread of corona virus. It could be practiced effectively
in current situation when lockdown is eased to inspect persons in public
gatherings, shopping malls, etc. Automated inspection reduces manpower to
inspect the public and can be used in any place to ensure safety.
Country : India
1 Dr. S. P. Malarvizhi
Professor, Department of Computer Science and Engineering, Malla Reddy College of Engineering for Women, Hyderabad -500100, Telangana, India
Lin, Tsung-Yi, PiotrDollár, Ross Girshick, Kaiming He,
BharathHariharan, and Serge Belongie, "Type Pyramid Networks for Object
Detection," IEEE Conference Proceedings on Computer Vision and Pattern
Recognition, pp. 2117-2125. 2017.
Z. Wang, G. Wang, B. Huang, Z. Xiong, Q. Hong, H. Wu, P. Yi,
K. Jiang, N. Wang, Y. Peiet al., “Masked face recognition dataset and
application,” arXiv preprint arXiv:2003.09093, 2020.
Z.-Q. Zhao, P. Zheng, S.-t. Xu, and X. Wu, “Object detection
with deep learning: A review,”IEEE transactions on neural networks and learning
systems, vol. 30, no. 11, pp. 3212–3232, 2019.
Kumar, A. Kaur, and M. Kumar, “Face detection techniques: a
review,” Artificial Intelligence Review, vol. 52, no. 2, pp. 927–948,
2019.D.-H. Lee, K.-L. Chen, K.-H. Liou, C.-L. Liu, and J.-L. Liu, “Deep
learning and control algorithms.
Glass RJ, Glass LM, Beyeler WE, Min HJ. Targeted social
distancing architecture for pandemic influenza. Emerging Infectious Diseases.
2006; 12:1671–1681
S. Ge, J. Li, Q. Ye and Z. Luo, "Detection of Masked
Faces in the Wild with LLE-CNNs," 2017 IEEE Conference on Computer Vision
and Pattern Recognition (CVPR), Honolulu, HI, 2017, pp. 426-434.
S. S. Mohamed, N. M. Tahir and R. Adnan, "Background
modelling and background subtraction efficiency for object detection,"
2010 6th International Colloquium on Signal Processing & its Applications,
Mallaca City, 2010, pp. 1-6, doi: 10.1109/CSPA.2010.5545291.
S. S. Farfade, M. J. Saberian, and L. Li. Multi-view face
recognition using deep convolutional neural networks. In ACM ICMR, pages 643–
650, 2015
C.Fu, W.Liu, A.Ranga, A. Tyagi, A. Berg, "DSSD:
deconvolutional single shot detector model," arXiv preprint
arXiv:1701.06659, (2017).
