=Paper=
{{Paper
|id=Vol-3693/paper9
|storemode=property
|title=Review of Social Distancing and Face Mask of Coronavirus Spread
|pdfUrl=https://ceur-ws.org/Vol-3693/paper9.pdf
|volume=Vol-3693
|authors=Claudia M. Escobedo Alcázar,Jose E. Gutierrez Arias,Nataly J. Alvarez Cervantes,Rodrigo A. Canaza Pilco,Luz E. Gonzales Medina,Jhon E. Monroy Barrios,Wilder Nina Choquehuayta
|dblpUrl=https://dblp.org/rec/conf/jinis/AlcazarACPMBC23
}}
==Review of Social Distancing and Face Mask of Coronavirus Spread==
https://ceur-ws.org/Vol-3693/paper9.pdf
Review of Social Distancing and Face Mask of Coronavirus
Spread
Claudia M. Escobedo Alcázar1,† , Jose E. Gutierrez Arias1,† , Nataly J. Alvarez Cervantes1,† ,
Rodrigo A. Canaza Pilco1,† , Luz E. Gonzales Medina1,† , Jhon E. Monroy Barrios1,† and
Wilder Nina Choquehuayta1,*,†
1
Universidad Tecnológica del Perú, Arequipa, Perú
Abstract
Development of artificial intelligence applications has let to reduce the spread of COVID-19 in many countries.
The objective in this review is analysis of different solutions based on mask detection algorithms and social
distancing methods to combat to COVID-19. Method applied is to search eight databases namely Ebsco, Dynamed,
IEEE, IOP, Sage, Scopus, Science direct, Taylor, and Francis, and the run three sequences of search queries between
2019 and 2022. Results obtained using precise exclusion criteria and a selection strategy were applied to select
the 8578 articles and then obtained 48 articles were fully assessed and included in this review, and this number
only emphasized the insufficiency of research in this important area. After analyzing all the included studies,
the results were distributed according to the year of publication and the commonly used deep learning and
ML algorithms. The results found in all the papers were discussed to find the gaps in all the articles reviewed.
Characteristics, such as motivations, challenges, limitations, recommendations, case studies, and characteristics
and classes used were analyzed in detail. Conclusion is find showed that the growing emphasis on deep learning
and ML techniques in field, can provide the right environment for change and improvement.
Keywords
Coronavirus, Social Distance, Mask Detection, Machine Learning, Face Mask Detection
1. Introduction
The outbreak of the novel coronavirus infection (COVID-19) has caused concern around the world as it
causes illness, including death, and continues to spread from person to person in many countries [1]
CoVs are a large family of viruses, including Middle East Respiratory Syndrome (MERS) CoV and Severe
Acute Respiratory Syndrome (SARS) CoV [2]. On February 11, 2020, the World Health Organization
(WHO) declared ’COVID-19’ as a disease [3], Artificial intelligence (AI) is gradually changing practical
medicine and recent advances in digitized data collection, making machine learning (ML), computing
infrastructure and AI applications expand into areas previously reserved for human experts [4]. There-
fore, the main objective of this research is to be able to analyze the use of recognition and detection
systems that allow the identification of control measures established for the mitigation of SARS-CoV-2
contagion using masks and distancing as the main strategy of the study through the review of articles
on image processing and computer vision, using algorithms for a clear understanding of the prediction
and use of these computer tools in support of science and its application. The challenges and limitations
encountered will allow us to propose opportunities for improvement and a clear focus on the main
findings found in the information reviewed.
JINIS 2023: XXX International Conference on Systems Engineering, October 03–05, 2023, Arequipa, Peru
*
Corresponding author.
†
These authors contributed equally.
$ 1012757@utp.edu.pe (C. M. E. Alcázar); 1512915@utp.edu.pe (J. E. G. Arias); 1626631@utp.edu.pe (N. J. A. Cervantes);
1626828@utp.edu.pe (R. A. C. Pilco); c16401@utp.edu.pe (L. E. G. Medina); c19315@utp.edu.pe (J. E. M. Barrios);
c18795@utp.edu.pe (W. N. Choquehuayta)
0000-0002-4612-6050 (C. M. E. Alcázar); 0000-0001-7012-6459 (J. E. G. Arias); 0000-0002-4311-5292 (N. J. A. Cervantes);
0000-0002-0051-5878 (R. A. C. Pilco); 0000-0003-0402-8644 (L. E. G. Medina); 0000-0002-6676-9950 (J. E. M. Barrios);
0000-0002-6027-4973 (W. N. Choquehuayta)
© 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
2. Methods
This study followed the literature search style recommended by the Preferred Reporting Items for
Systematic Reviews and Meta-Analyses (PRISMA) method [5], where uses 8 databases, namely Ebsco,
Dynamed, IEEE, IOP, Sage, Scopus, Science Direct, Taylor and Francis. Trusted science and technology
journals containing contemporary research papers in computer science, electronics, and interdisciplinary
research. The results of this study can help researchers in the area of image processing and computer
vision to know detailed information on technological advances with existing image detection and
recognition systems.
2.1. Search Strategy
A bibliographic search in English was carried out in the eight academic repositories between the years
2019 and 2022, considering precise exclusion criteria. The selection of the research articles was due
to the similar characteristics with our research, considering that the new methods identified require
greater computational power. This research carried out a search strategy using several keywords related
to the coronavirus and keywords related to the detection, diagnosis and classification of masks under
the concept of AI and ML. We use these query methods to improve the search and investigation of
distancing and mask applications for various AI and machine learning systems.
2.2. Inclusion criteria
• Articles are journals or conferences in English.
• The focus is on the development of various applications, systems, algorithms, methods and
technologies in artificial intelligence and machine learning.
• Development focused on the detection and classification of masks and social distancing.
2.3. Exclusion criteria
• Articles minor to the year 2019.
• Less with less than 15 bibliographical references.
2.4. Study Selection
The process begins with the elimination of duplicate articles, Unique articles were screened by title
and abstract to verify their compliance with our inclusion and exclusion criteria. The relevant articles
have been carefully read. The process of collecting, extracting research data, and developing a review
document.
2.5. Data extraction and classification
Given the multidisciplinary topic of this systematic review, data extraction and classification were
performed for selected studies, including CoV data using AI applications, especially ML techniques,
to assess the effectiveness of viruses in detection, diagnosis, prevention and classification. Enhanced
data factors were extracted from the academic literature, including author nationality, publication
date, number of articles per year, and number of articles in the database. To provide a comprehensive
understanding of CoV, this study The discussed CoV and the growing scale of the global pandemic in
the context of artificial intelligence are analyzed using various ML and data mining algorithms, such as
classification, regression, and prediction. For each study, the document distinguishes the name of the
significant characteristics, the evaluation methods used and the exact status of each method. From the
analyzed literature, brief motivations, challenges, limitations and recommendations have been extracted
to address the serious health problems associated with CoV.
2.6. Results
The results of the search query performed in this study are shown in Figure 1. During data collection,
four queries were performed to cover all databases and search mechanisms. The first result included
8578 articles from eight databases. The number of duplicate articles in all databases was 626. The first
process was to select articles based on a relevant keyword selection filter, which resulted in 1141 articles.
The second process was to select articles based on title and then map inclusion and exclusion criteria,
resulting in 351 articles. The third process carried out was to select articles by abstract, reading each
one for its selection, which resulted in 275 articles. The final process was to read all the articles in their
entirety, with only 48 articles meeting the inclusion and exclusion criteria.
Table 1
Three types of the Boolean search query
Seq. Query Details Terms Result of Databases Final Results
Ebsco = 151
Dynamed = 0
IEEE = 143
(‘coronavirus’ OR ‘coronaviridae’ OR ‘covid’) AND
IOP = 38
1st query (‘detection system’ OR ‘detection’) AND 7423 - 508 (duplicate) = 6915
Sage = 959
(’social distance’ OR ’social distancing’)
Scopus = 1981
Science direct = 2782
Taylor y francis = 1369
Ebsco = 121
Dynamed = 0
(‘coronavirus’ OR ‘coronaviridae’ OR ’covid’ ) AND (‘detection’ OR IEEE = 160
‘classification’) AND (‘machine learning’ OR IOP = 12
2nd query 981 - 109 (duplicate) = 872
‘artificial intelligence’ OR ’AI’ OR ’ML’) AND (’mask detection’ OR Sage = 113
’face mask detection’) Scopus = 385
Science direct = 44
Taylor y francis = 146
Ebsco = 15
Dynamed = 0
(‘coronavirus’ OR ‘coronaviridae’ OR ’covid’ ) AND
IEEE = 34
(‘detection’ OR ‘classification’) AND
IOP = 0
3rd query (’social distance’ OR ’social distancing’) AND 174 - 9 (duplicate) = 165
Sage = 0
(’mask detection’ OR ’face mask detection’) AND (‘machine learning’ OR
Scopus = 101
‘artificial intelligence’ OR ’AI’ OR ’ML’)
Science direct = 24
Taylor y francis = 0
Final result for all queries 7952 articles
3. Statistical results
The results of metodology from Figure 1 has 48 papers that then realizes a histogram of tecniques and
datasets in each query. Each query delimited a group of interested acording to proposals and datasets,
1st query (red color) is only papers that proposal and datasets to social distance, 2do query (green color)
is face mask and 3rd query (blue color) are proposal that include social distance and face mask. In the
Figure 2 shows to summary of algorithms and methods used in the literature review for 1st, 2nd and
3rd query, when has techniques like YOLO V3, Faster R-CNN, YOLO V2, MobileNet, DensNet, YOLO
V4, YOLO V5 and YOLO-LITE, where consideres that techniques with best result using mAP metric.
The analysis from histogram shows that YOLO V3 is that more used with 12, 12 and 3 papers to 1st, 2nd
and 3rd query respectively.
Acording to state of art YOLO V5 has better in accuracy and inference time compared YOLO V3 but
in Figure shows that YOLO V5 is only uses in 1, 0 and 1 paper to 1st, 2nd and 3rd query respectively.
For 3rd query that include both a proposal to face mask and social distance have 3, 1, 1, 0, 1 papers to
Figure 1: Schematic of the approach to identify, screen and include relevant studies.
YOLO V3 [6, 7, 8], Faster R-CNN [9], MobileNet [10] and YOLO V5 [11]. Analysing Figure 3, it shows to
summary of datasets used in the literature review for 1st, 2nd and 3rd query, when has techniques like
COCO, Oxford Town Center Dataset, KITTI 3D dataset, CCTV, RMFD, Medical Masks Dataset, Own
dataset, Youtube, FaceMask, SAI-YOLO, Face dataset Kaggle, Benchmark mask dataset and Imagenet-21k.
The analysis from histogram show that COCO is that more used with 5 papers to 1st query, Own dataset
with 4 and 2 papers to 2nd and 3rd query respectively. For 3rd query that include both a proposal
to mask fask and social distance have 1, 2, 1 papers to Own dataset [10, 8, 11] and Benchmark mask
dataset [9].
15
Q1
1212 Q2
Q3
10
Total
5
3
2 22 2
1 1 1 1 1 1 1
0 00 00 00 0 00
0
YOLO V3 Faster YOLO V2 MobileNet DensNet YOLO V4 YOLO V5 YOLO-
R-CNN Technique LITE
Figure 2: Histogram of algorithms and methods used in the literature review for 1st, 2nd and 3rd query
15
Q1
Q2
Q3
10
Total
5
5 4 4 4
3 3
2
1 1 1 1 1 1 1 1 1 1
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0
COCO Oxford KITTI 3D RMFD Medical Own FaceMask
SAI-YOLO Face BenchmarkCCTV From Imagenet-
Town dataset Masks datasetDataset dataset mask Internet 21k
Center Dataset Kaggle dataset
Dataset
Figure 3: Histogram of datasets used in the literature review for 1st, 2nd and 3rd query.
4. Discussion
4.1. Techniques
In Table 2 shows results of analyzes techniques from 45 papers according to 1st, 2nd 3rd query from
Table 1 using methodology in section of methods. The first 17 papers from Table 2 shows most used
algorithms for the recognition of social distancing are YOLO, Faster R-CNN and SSD with evaluations
oriented on the average accuracy (mAP) and FPS to compare which algorithm gives better results in
different environments. [12, 13, 14, 15, 16, 17, 18, 10, 19, 20, 21, 22, 23, 24]. YOLO is the most used
algorithm, with the percentage of use by versions being (YOLO V2 is with 5%, YOLO V3 with 75% and
YOLO V4 with 10%), but those that use (F-RCNN and SDD) [25, 26, 24, 27, 21] go for the performance
side and response time in FPS with higher results. Regarding the versions, it can be seen that there is a
tendency to look for the latest updates.
Then 22 papers from Table 2 shows most used algorithms for face mask detection as YOLO, Mobile
Net, ResNet, and topen-source computer vision library of python OpenCV. YOLO V3 [28], YOLO V4 [29]
[30] and YOLO V5 [31] [11]; last one shows a high level of accuracy, major that 95%. After the analysis,
it can notice the article [32] has the highest accuracy with 99.7%, the algorithm used was MobileNetV2
with the dataset Face-Mask-Detection. Last 6 papers from Table 2 shows about proposal that include face
mask detection and social distancing where it is analyzed 4 of them work with YOLO V3 [9, 6, 7, 8], with
an average of 94.5% accuracy corresponding to the test evaluation scope; Faster R-CNN algorithm [9],
with an average accuracy of 99%; MobileNet algorithm [10] with an average of 99%, YOLO V5 [11], with
a precision/recall of 0.6/0.98 and finally the ResNet algorithm [10, 7, 11], with an average of 98.5%.
4.2. Datasets
In Table 4 includes a short description of the research purpose of Face mask dataset. In addition, it
shows the principal classes used, images, labels, total weight, metrics and access type. Next in Table 3
shows a brief of the main features of social distancing dataset as: Duration, resolution, weight, metric
and access type.
4.3. Challenges and limitations
The studies carried out to develop applications that use artificial intelligence techniques present many
challenges and limitations in research repositories that must be addressed with urgency and interest.
The current challenges are related to the transmission and contagion of COVID-19 due to the lack of
Table 2
Algorithms and proposals of Social Distancing, Face Mask Detection and mixed in the COVID-19 context.
Ref. ML Classification algorithms Evaluation Accuracy
[12] YOLO V3, SSD and Faster RCNN accuracy by mAP and FPS 46.5% and 55.3%
[13] YOLO V3 - -
[33] Faster R-CNN, R-FCN and SSD screen refresh rate 2 FPS
[14] YOLO V3 detection with various angles 90%
[15] YOLO V3 - -
[16] YOLOV3 mean average precision 57.9%
[17] YOLO V3, Dist-YOLO V3 G and Dist-YOLO V3 W mean average precision 74.3% and 77.1%
[18] YOLO V2, Fast R-CNN and R-CNN accuracy, precision and recall 86% and 96%
[19] YOLO V4, YOLO V3 and Faster RCNN performance metrics acurracy 0.96, 0.84 and 0.6
[20] YOLO V3 and R-CNN performance metrics FPS 0.5 and 0.45
[21] YOLO V3, Faster RCNN and SSD evaluation acurrancy 0.5, 0.59 and 0.35
[27] YOLO V3, SSD and Faster RCNN acurrancy 0.80, 078 and 074
[22] YOLO V3 and YOLO V4 acurrancy 94.75% and 95%
[23] YOLO-LITE, R-CNN and Darknet-53 - -
[25] OpenCV HOG averaged error (cm) 6.72
[26] YOLO V3 and MobilNetSSD acurracy and Speedd comparison 30 / 26.66
[24] YOLOV3, FPN FRCNN and SSD300 performance metrics mAP and FPS 55.3 / 59.1 / 41.2
[34] MobileNet accuracy 94.5%
[35] Openpose and FMRN accuracy daytime/nighttime 95.8% / 94.6%
[36] MobileNet (HoG) accuracy 95.67%
[37] YOLO Nano approach and Gaussian Mixture Model - -
[38] YOLO V3 accuracy 98%
[31] YOLO V5 accuracy 97.9%
[29] YOLO V4 and SpineNet-190 accuracy 94.7%
[30] YOLO v4, Tiny and Lightweight mAP/ AP 86% / 88%
[39] Gabor Wavelet accuracy 97%
[32] MobileNetV2 accuracy 99.7%
[40] OpenCV and TensorFlow accuracy 97%
[41] ResNet50 and ResNet101 accuracy 91%
[42] OpenCV, Keras and TensorFlow accuracy 97.05%
[11] YOLO V5 and DBSCAN accuracy precision and recall 85%, 97% and 80%
[43] Computer vision accuracy/ precision 9% / 89%
[44] TensorFlow and MobileNetV2 accuracy 99.64%
[28] Haar Cascade Classifier connection speed (seconds) 0.001695977
[45] ResNet50 accuracy 98.2%
[46] Quantum Transfer Learning and ResNet-18 accuracy 99.05%
[47] mAP, MaskedFaceNet and MaskedFaceNet Light 0.9813 / 0.9812
[48] ResNet-50 accuracy 98.74%
[49] Pynq- YOLO-Net accuracy 97%
[9] Faster R-CNN and YOLOv3 Precision with mask/Without mask 0.99 / 0.88
[10] MobileNet and ResNet F1-score/sensitivity/ specificity/accuracy 0.99 / 0.99 / 0.99 / 1.0
[6] YOLO V3 - -
[7] YOLO V3 and Resnet50 - -
[8] YOLO V3 accuracy/F1 score 0.9120 / 0.9079
[11] YOLO V5 and Resnet50 Precision/recall 0.6 / 0.98
Table 3
Summary of Social Distaning Datasets in the COVID-19 context.
Dataset Time Resolution Size Best metric Access
Oxford Town Center [50] 5 Min. 1920 X 1080 1.04 GB 79.71 public
Mall [51] 33 Min. 640 X 480 60.2 MB 79.41 public
Train Station [52] 33 Min. 720 X 480 140 MB 79.50 public
Ground truth Meter 1 [53] 1 Min. 720 X 480 300 MB 70.22 public
Ground truth Meter 2 [53] 1 Min. 720 X 480 253 MB 70.15 public
Ground truth Meter 3 [53] 13 Sec. 720 X 480 92 MB 70.05 public
Table 4
Summary of Face Mask Datasets in the COVID-19 context.
Dataset Purpose Class Images Labels Size Access
Right face mask wearing 24,603 24,603
MAFA [54] Mask wearing detection Wrong face mask wearing 1,204 1,204 2.32 Gb Public
No face mask wearing 3,645 3,645
Train - MHP 2,500 2,500
Multi Human Parsing [55] People detection - Private
Validation - MHP 2,500 2,500
Train 162,770 162,770
Celeb [56] Facial attributes recognition Test 19,962 19,962 1.62Gb Public
Validation 19,867 19,867
Face detection
WilderFace [56] 61 event types 32,203 393,703 - Public
with exposure variability
WMD [57] Mask wearing detection Face with mask 7804 26,403 673.1MB Public
Face with mask
WMC [57] Mask wearing detection 38145 - 154.9 MB Public
and background
Train
FaceDataset [53] Mask wearing detection 4054 16216 320.7MB Public
Validation
Train - MHP 2,500 2,500
MaskedFace-Net [53] Mask wearing detection - Public
Validation - MHP 2,500 2,500
knowledge and complexity of this epidemic. Databases on COVID-19 are difficult for researchers to
access and have characteristics that often do not meet the needs of researchers or are synthetic databases.
Database construction requires the processing of large volumes of data that includes manual evaluation
of unstructured data. Other challenges are related to the transparency of information from governments
and public entities in charge of sanitary control, which could generate a bias in the results. Another
challenge present in research is the similar behavior of COVID-19 with other traditional diseases. Points
to take into account in the correct detection and accuracy given that there are inconveniences such
as children or babies because most of the measurements are not detected within the ’person’ regime.
Another important point to take into consideration with the shadows, given that in some detections
made by the angle of the camera, it is filtered as a person itself. These are cases that cause an impact
within the percentage of final accuracy for applications.
4.4. Recommendations
The objective of this research is to help researchers learn about research on artificial intelligence issues
and limitations and advances to mitigate the contagion of the pandemic generated by COVID-19 and
thus be able to contribute to generating new research. Studies, such as the one by [32] propose a deep
convolutional neural network (CNN) based on the MobileNetV2 architecture as a learning algorithm.
The results show an accuracy of 99.7 % in mask detection with a run time of 1.54 s. Another study [46]
uses ResNet-18; with 99.05% accuracy in the classification of protective masks. Finally [10] focuses
on implementing a face mask detection and social distancing model as an integrated vision system.
Pre-trained models such as MobileNet, ResNet Classifier, and VGG with an F1 score of 99%, a sensitivity
of 99%, a specificity of 99%, and an accuracy of 100%.
5. Conclusions
The research carried out allows knowing the technological advances in the area of artificial intelligence
aimed at solving problems generated by COVID-19. This research analyzed the different solution pro-
posals based on mask detection algorithms and social distancing methods, considering the performance
of machine learning models. Additionally, recommendations were established that will serve as a guide
for the selection of research proposals in the area of artificial intelligence that try to solve problems of
COVID-19. Finally, it is suggested to use specific terms in database queries to obtain optimal results
considering the quotes and logical connectors.
6. Acknowledgments
Thanks to the “Universidad Tecnológica del Perú (UTP)”, for supporting the development of technology
and scientific research.
References
[1] D. Wu, T. Wu, Q. Liu, Z. Yang, The sars-cov-2 outbreak: what we know, International Journal of
Infectious Diseases 94 (2020) 44–48.
[2] Y. Fan, K. Zhao, Z.-L. Shi, P. Zhou, Bat coronaviruses in china, Viruses 11 (2019) 210.
[3] C. for Disease Control, Prevention, et al., Centers for disease control and prevention coronavirus
disease 2019 (covid-19) 2020, United States: Centers for Disease Control and Prevention (2020).
[4] K.-H. Yu, A. L. Beam, I. S. Kohane, Artificial intelligence in healthcare, Nature biomedical
engineering 2 (2018) 719–731.
[5] D. Moher, A. Liberati, J. Tetzlaff, D. G. Altman, P. Group*, Preferred reporting items for systematic
reviews and meta-analyses: the prisma statement, Annals of internal medicine 151 (2009) 264–269.
[6] S. V. Shetty, K. Anand, S. Pooja, K. Punnya, M. Priyanka, Social distancing and face mask detection
using deep learning models: A survey, in: 2021 Asian Conference on Innovation in Technology
(ASIANCON), IEEE, 2021, pp. 1–6.
[7] S. Subhash, K. Sneha, A. Ullas, D. Raj, A covid-19 safety web application to monitor social
distancing and mask detection, in: 2021 IEEE 9th Region 10 Humanitarian Technology Conference
(R10-HTC), IEEE, ????, pp. 01–06.
[8] S. Srinivasan, R. R. Singh, R. R. Biradar, S. Revathi, COVID-19 monitoring system using social dis-
tancing and face mask detection on surveillance video datasets, in: 2021 International Conference
on Emerging Smart Computing and Informatics (ESCI), IEEE, 2021. doi:10.1109/esci50559.
2021.9396783.
[9] S. Meivel, N. Sindhwani, R. Anand, D. Pandey, A. A. Alnuaim, A. S. Altheneyan, M. Y. Jabarulla,
M. E. Lelisho, Mask detection and social distance identification using internet of things and faster
r-cnn algorithm, Computational Intelligence and Neuroscience 2022 (2022).
[10] S. Teboulbi, S. Messaoud, M. A. Hajjaji, A. Mtibaa, Real-time implementation of ai-based face mask
detection and social distancing measuring system for covid-19 prevention, Scientific Programming
2021 (2021).
[11] I. S. Walia, D. Kumar, K. Sharma, J. D. Hemanth, D. E. Popescu, An integrated approach for moni-
toring social distancing and face mask detection using stacked resnet-50 and yolov5, Electronics
10 (2021) 2996.
[12] M. M. Özbek, M. Syed, I. Öksüz, Subjective analysis of social distance monitoring using yolo v3
architecture and crowd tracking system., Turkish Journal of Electrical Engineering & Computer
Sciences 29 (2021).
[13] G. Shalini, M. K. Margret, M. S. Niraimathi, S. Subashree, Social distancing analyzer using computer
vision and deep learning, in: Journal of Physics: Conference Series, volume 1916, IOP Publishing,
2021, p. 012039.
[14] P. Somaldo, F. A. Ferdiansyah, G. Jati, W. Jatmiko, Developing smart covid-19 social distancing
surveillance drone using yolo implemented in robot operating system simulation environment, in:
2020 IEEE 8th R10 Humanitarian Technology Conference (R10-HTC), IEEE, 2020, pp. 1–6.
[15] Y. C. Hou, M. Z. Baharuddin, S. Yussof, S. Dzulkifly, Social distancing detection with deep learning
model, in: 2020 8th International Conference on Information Technology and Multimedia (ICIMU),
IEEE, 2020, pp. 334–338.
[16] J. Shah, H. Gala, K. Pattni, P. Kanani, Arduino based temperature, mask wearing and social
distance detection for covid-19, in: International Conference on Internet of Things and Connected
Technologies, Springer, 2021, pp. 148–161.
[17] M. Vajgl, P. Hurtik, T. Nejezchleba, Dist-yolo: Fast object detection with distance estimation,
Applied Sciences 12 (2022) 1354.
[18] S. Saponara, A. Elhanashi, A. Gagliardi, Implementing a real-time, ai-based, people detection and
social distancing measuring system for covid-19, Journal of Real-Time Image Processing 18 (2021)
1937–1947.
[19] J. Li, Z. Wu, The application of yolov4 and a new pedestrian clustering algorithm to implement
social distance monitoring during the covid-19 pandemic, in: Journal of Physics: Conference
Series, volume 1865, IOP Publishing, 2021, p. 042019.
[20] R. J. P. Garcia, J. N. Ronato, J. Flores Villaverde, Monitoring of people capacity in an establishment
using yolov3 algorithm, in: 2021 The 6th International Conference on Information and Education
Innovations, 2021, pp. 118–124.
[21] R. Magoo, H. Singh, N. Jindal, N. Hooda, P. S. Rana, Deep learning-based bird eye view social
distancing monitoring using surveillance video for curbing the covid-19 spread, Neural Computing
and Applications 33 (2021) 15807–15814.
[22] K. Bhambani, T. Jain, K. A. Sultanpure, Real-time face mask and social distancing violation
detection system using yolo, in: 2020 IEEE Bangalore Humanitarian Technology Conference
(B-HTC), IEEE, 2020, pp. 1–6.
[23] F. Mercaldo, F. Martinelli, A. Santone, A proposal to ensure social distancing with deep learning-
based object detection, in: 2021 International Joint Conference on Neural Networks (IJCNN), 2021,
pp. 1–5. doi:10.1109/IJCNN52387.2021.9534231.
[24] S. Das, A. Nag, D. Adhikary, R. J. Ram, B. Aravind, S. K. Ojha, G. Hegde, Computer vision-based
social distancing surveillance with automated camera calibration for large-scale deployment, in:
2021 IEEE 18th India Council International Conference (INDICON), IEEE, 2021, pp. 1–6.
[25] H. Ziran, N. Dahnoun, A contactless solution for monitoring social distancing: A stereo vision
enabled real-time human distance measuring system, in: 2021 10th Mediterranean Conference on
Embedded Computing (MECO), IEEE, 2021, pp. 1–6.
[26] M. Ismail, T. Najeeb, N. S. Anzar, A. Aditya, B. R. Poorna, Using computer vision to detect violation
of social distancing in queues, in: S. Smys, V. E. Balas, R. Palanisamy (Eds.), Inventive Computation
and Information Technologies, Springer Nature Singapore, Singapore, 2022, pp. 771–779.
[27] N. S. Punn, S. K. Sonbhadra, S. Agarwal, G. Rai, Monitoring covid-19 social distancing with
person detection and tracking via fine-tuned yolo v3 and deepsort techniques, 2020. URL: https:
//arxiv.org/abs/2005.01385. doi:10.48550/ARXIV.2005.01385.
[28] A. Rahmatulloh, R. Gunawan, H. Sulastri, I. Pratama, I. Darmawan, Face mask detection using
haar cascade classifier algorithm based on internet of things with telegram bot notification, in:
2021 International Conference Advancement in Data Science, E-learning and Information Systems
(ICADEIS), IEEE, 2021, pp. 1–6.
[29] Z. Cao, M. Shao, L. Xu, S. Mu, H. Qu, Maskhunter: real-time object detection of face masks during
the covid-19 pandemic, IET Image Processing 14 (2020) 4359–4367.
[30] Z. Zhao, K. Hao, X. Ma, X. Liu, T. Zheng, J. Xu, S. Cui, Sai-yolo: A lightweight network for real-time
detection of driver mask-wearing specification on resource-constrained devices, Computational
Intelligence and Neuroscience 2021 (2021).
[31] G. Yang, W. Feng, J. Jin, Q. Lei, X. Li, G. Gui, W. Wang, Face mask recognition system with
yolov5 based on image recognition, in: 2020 IEEE 6th International Conference on Computer and
Communications (ICCC), IEEE, 2020, pp. 1398–1404.
[32] S. Chokri, W. B. Daoud, W. Hanini, S. Mahfoudhi, A. Makhlouf, Ai-based system for the detection
and prevention of covid-19, International Journal of Advanced Computer Science and Applications
13 (2022).
[33] O. Karaman, A. Alhudhaif, K. Polat, Development of smart camera systems based on artificial
intelligence network for social distance detection to fight against covid-19, Applied Soft Computing
110 (2021) 107610.
[34] S. V. Militante, E. L. M. Marin, G. V. Canja, G. J. B. Paglingayen, M. J. M. Saballa-Marin, Deep
learning application of automated facemask classification and physical-distancing detection, in:
2021 Fourth International Conference on Vocational Education and Electrical Engineering (ICVEE),
IEEE, 2021, pp. 1–5.
[35] H. Lin, R. Tse, S.-K. Tang, Y. Chen, W. Ke, G. Pau, Near-realtime face mask wearing recognition
based on deep learning, in: 2021 IEEE 18th Annual Consumer Communications & Networking
Conference (CCNC), IEEE, 2021, pp. 1–7.
[36] S. K. Dey, A. Howlader, C. Deb, Mobilenet mask: a multi-phase face mask detection model to
prevent person-to-person transmission of sars-cov-2, in: Proceedings of International Conference
on Trends in Computational and Cognitive Engineering, Springer, 2021, pp. 603–613.
[37] E. Nasiri, M. Milanova, A. Nasiri, Video surveillance framework based on real-time face mask
detection and recognition, in: 2021 International Conference on INnovations in Intelligent SysTems
and Applications (INISTA), IEEE, 2021, pp. 1–7.
[38] B. Sathyabama, A. Devpura, M. Maroti, R. S. Rajput, Monitoring pandemic precautionary protocols
using real-time surveillance and artificial intelligence, in: 2020 3rd International Conference on
Intelligent Sustainable Systems (ICISS), IEEE, 2020, pp. 1036–1041.
[39] P. Durga, G. Divya, D. Ayeshwariya, P. Sivakumar, et al., Gabor-deep cnn based masked face recog-
nition for fraud prevention, in: 2021 5th International Conference on Computing Methodologies
and Communication (ICCMC), IEEE, 2021, pp. 990–995.
[40] S. V. Militante, N. V. Dionisio, Deep learning implementation of facemask and physical distancing
detection with alarm systems, in: 2020 Third International Conference on Vocational Education
and Electrical Engineering (ICVEE), IEEE, 2020, pp. 1–5.
[41] B. Bhattarai, Y. Raj Pandeya, J. Lee, Deep learning-based face mask detection using automated gui
for covid-19, in: 2021 6th International Conference on Machine Learning Technologies, 2021, pp.
47–57.
[42] G. Kaur, R. Sinha, P. K. Tiwari, S. K. Yadav, P. Pandey, R. Raj, A. Vashisth, M. Rakhra, Face mask
recognition system using cnn model, Neuroscience Informatics (2021) 100035.
[43] G. T. Draughon, P. Sun, J. P. Lynch, Implementation of a computer vision framework for tracking
and visualizing face mask usage in urban environments, in: 2020 IEEE International Smart Cities
Conference (ISC2), IEEE, 2020, pp. 1–8.
[44] E. Kontellis, C. Troussas, A. Krouska, C. Sgouropoulou, Real-time face mask detector using
convolutional neural networks amidst covid-19 pandemic, in: 1st International Conference on
Novelties in Intelligent Digital Systems, NIDS 2021, 2021, pp. V–VI.
[45] S. Sethi, M. Kathuria, T. Kaushik, A real-time integrated face mask detector to curtail spread of
coronavirus, CMES-Computer Modeling in Engineering and Sciences (2021) 389–409.
[46] C. Soto-Paredes, J. Sulla-Torres, Hybrid model of quantum transfer learning to classify face images
with a covid-19 mask, International Journal of Advanced Computer Science and Applications 12
(2021).
[47] S. Prasad, Y. Li, D. Lin, D. Sheng, maskedfacenet: A progressive semi-supervised masked face
detector, in: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision,
2021, pp. 3389–3398.
[48] E. Ryumina, D. Ryumin, D. Ivanko, A. Karpov, A novel method for protective face mask detec-
tion using convolutional neural networks and image histograms., International Archives of the
Photogrammetry, Remote Sensing & Spatial Information Sciences (2021).
[49] Y. Said, Pynq-yolo-net: An embedded quantized convolutional neural network for face mask
detection in covid-19 pandemic era, Int. J. Adv. Comput. Sci. Appl 11 (2020) 100–106.
[50] B. Benfold, I. Reid, Stable multi-target tracking in real-time surveillance video, in: CVPR 2011,
2011, pp. 3457–3464. doi:10.1109/CVPR.2011.5995667.
[51] K. Chen, C. C. Loy, S. Gong, T. Xiang, Feature mining for localised crowd counting., in: Bmvc,
volume 1, 2012, p. 3.
[52] B. Zhou, X. Wang, X. Tang, Understanding collective crowd behaviors: Learning a mixture
model of dynamic pedestrian-agents, in: 2012 IEEE Conference on Computer Vision and Pattern
Recognition, IEEE, 2012, pp. 2871–2878.
[53] Video dataset for covid-19 social distancing and human detection validation, https://data.mendeley.
com/datasets/xh6m6gxhvj/1, 11 Nov 2020. doi:10.17632/xh6m6gxhvj.1.
[54] A. S. Joshi, S. S. Joshi, G. Kanahasabai, R. Kapil, S. Gupta, Deep learning framework to detect face
masks from video footage, in: 2020 12th International Conference on Computational Intelligence
and Communication Networks (CICN), IEEE, 2020, pp. 435–440.
[55] T. Meenpal, A. Balakrishnan, A. Verma, Facial Mask Detection using Semantic Segmentation, 2019
4th International Conference on Computing, Communications and Security, ICCCS 2019 (2019)
1–5. doi:10.1109/CCCS.2019.8888092.
[56] R. C. Atmudi, A. A. Rao, Covid-19 Face Mask Prediction using Machine Learning Techniques
Covid-19 Face Mask Prediction using Machine Learning Techniques (2020).
[57] B. Wang, Y. Zhao, C. P. Chen, Hybrid transfer learning and broad learning system for wearing
mask detection in the covid-19 era, IEEE Transactions on Instrumentation and Measurement 70
(2021) 1–12.