Abstract
COVID-19 is indeed newsworthy as it has reached a global emergency with an impending spread. Wearing a face covering and maintaining physical distance is a smart move recommended by the WHO. People infected with COVID-19 have difficulty breathing with shortness of breath. Environmental elements of those involved can be contaminated by infectious droplets. It is mandatory to wear a cover and follow a physical withdrawal, but many residents ignore the rules. In such situations, regular checks for facial coverings openly places and it are normal to force fines. As article recognition has unfurled to be a receptive biometric process, it has been broadly applied in observation, security, independent driving, and so on. With the fast improvement of profound learning models, object locators are exceptionally reasonable to foster social removing and facial covering indicators to direct the group by means of CCTV and observation cameras. The paper studies different profound learning organizations to foster such locators. In this review, the current article discovery models utilized for reconnaissance and individuals location are examined. The one-stage and two-stage identifiers alongside their applications and execution are framed by exhaustive way.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Petrović & Kocić, Đ. (2020). IoT-located scheme for COVID-19 household security listening,” Preprint), IcETRAN (pp. 1–6). In press.
Qin, B., & Li, D. (2020). Identifying facemask-tiring condition utilizing countenance excellent-determination accompanying categorization network for fear that COVID-19. Sensors, 20(18).
Rezaei, M., & Azarmi, M. (2020). Deep social: Public outpacing listening and contamination risk appraisal in COVID-19 universal. Applied Sciences, 10(21), 7514.
Yadav, S. (2020). Deep education located dependable friendly outpacing and face mask discovery honestly districts for COVID-19 security directions devotion. International Journal for Research in Applied Science and Engineering Technology, 8(7), 1368–1375.
Ahmed, I., Ahmad, A., & Jeon, G. (2020). An IoT located deep education foundation for early estimate of Covid-19. IEEE Internet of Things Journal, 8(1).
Rao, T. V. N., Sai, G. V., Chenna, M., & Reddy, M. S. (2020). An request for the discovery of COVID-19 face mask utilizing OpenCV. High Technology Letters, 26(7), 965–975.
Loey, M., Manogaran, G., Taha, M. H. N., & Khalifa, N. E. M. (2021). A composite deep transfer education model accompanying machine intelligence orders for face mask discovery in the stage of the COVID-19 universal. Measurement, 167, Article ID 108288.
Qin, B., & Li, D. (2020). Identifying facemask-tiring condition utilizing concept excellent-determination accompanying categorization network for fear that COVID-19. Sensors, 20(18), 5236.
Wang, Z., Wang, P., Louis, P. C., Wheless, L. E., & Huo, Y. (2021) Wearmask: Fast in-portal face mask discovery accompanying serverless edge estimating for COVID-19. https://arxiv.org/antilockbrakingsystem/2101.00784
Zhang, X., Saleh, H., Younis, E. M., Sahal, R., & Ali, A. A. (2020). Predicting coronavirus universal in legitimate-period utilizing machine intelligence and grown dossier flooding whole. Complexity, 10 pages.
Razavi, M., Alikhani, H., Janfaza, V., Sadeghi, B., & Alikhani, E. (2021). An mechanical order to monitor the material distance and face mask tiring of explanation traders in COVID-19 universal. https://arxiv.org/antilockbrakingsystem/2101.01373.
Srinivasan, S., Rujula Singh, R., Biradar, R. R., & Revathi, S. (2021). COVID-19 Monitoring system utilizing social distancing and face mask detection on surveillance program datasets. In 2021 International Conference on Emerging Smart Computing and Informatics (ESCI), pp. 449–455. https://doi.org/10.1109/ESCI50559.2021.9396783
Porumbel, H., & Kuntz. (2011). An effective treasure for calculating the distance middle from two points close partitions. https://doi.org/10.1016/j.dam.2010.09.002
McCloskey, B., Zumla, A., Ippolito, G., Blumberg, L., Arbon, P., Cicero, A., Endericks, T., Lim, P. L., & M. Borodina (2020). Mass accumulation occurrences and lowering the further all-encompassing spread of COVID-19: A governmental and community health crisis.
Chen, H., Yang, B., Pei, H., & Liu, J. (2019). Next Generation technology for epidemic prevention and control: Data-driven contact tracking. IEEE Access, 7, 2633–2642. https://doi.org/10.1109/ACCESS.2018.2882915
Chen, K., Wang, J., Pang, J., Cao, Y., Xiong, Y., Li, X., Sun, S., Feng, W., Liu, Z., Xu, J., & Zhang, Z. (2019). MMDetection: Open MMLab detection toolbox and benchmark. arXiv:1906.07155
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Sangeetha, T., Miruthula, V., Kavimalar, C., Aakash, V. (2023). Face Mask Detection and Social Distancing Using Machine Learning with Haar Cascade Algorithm. In: Shakya, S., Du, KL., Ntalianis, K. (eds) Sentiment Analysis and Deep Learning. Advances in Intelligent Systems and Computing, vol 1432. Springer, Singapore. https://doi.org/10.1007/978-981-19-5443-6_72
Download citation
DOI: https://doi.org/10.1007/978-981-19-5443-6_72
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-5442-9
Online ISBN: 978-981-19-5443-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)