International Journal of Nonlinear Analysis and Applications
Social distance in object detection: Survey based on cutting-edge deep learning approach
Document Type : Research Paper
Authors
Department of Computer Science, College of Science, University of Diyala, Baqubah, Iraq
Abstract
Many health systems face extraordinary problems because of the ongoing COVID-19 outbreak and new variations. Multiple regulatory authorities have made it mandatory to maintain a safe distance, particularly in public settings where large groups of people are likely to come into contact, such as sports arenas, public transportation, workplaces as well as shopping malls. Nevertheless, keeping a safe distance (two meters), adjusting multiple model detection errors or accuracy as well as deployment prerequisites, great number of people, facial expression, view angle, low-resolution images, detection model deployment on computers having restricted processing power, and the shortage of a real-world dataset have all made compliance and adherence to proper distancing social difficult. As a result, this survey examines and contrasts the most important past deep learning (DL)-based social distance research. Here, the survey presents a new fine-grained taxonomy that classifies the present state-of-the-art DL-based object detection for detecting distance in terms of several dimensions, such as detection, input data, evaluation methodologies, as well as testing, based on a thorough review. Each facet is then divided into categories based on a variety of factors. In addition, this survey analyses and evaluates the associated experimental techniques suggested as DL-based object detection. Finally, this survey examines DL's role in social distance, object detection datasets impact, as well as the proposed approaches efficacy by assessing the experimental research. The results show that more work is needed to enhance the existing state-of-the-art. Ultimately, open research difficulties are recognized, as well as prospective DL research areas are suggested for future research.
Keywords
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learning and a single motionless time of flight camera, PLoS One 16 (2021), no. 2, 1–19.
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networks, Adv. Neural Inf. Process. Syst. 28 (2015).[43] B. Roy, S. Nandy, D. Ghosh, D. Dutta, P. Biswas and T. Das, MOXA: A deep learning based unmanned approach
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social distancing monitoring in COVID-19 pandemic, IEEE Trans. Multimed. 24 (2021), 1–16.
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automated breast ultrasound, Biomed. Signal Process. Control 68 (2021), 102677.
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(2018).
the use of face mask in preventing the spread of COVID-19, Int. J. Nurs. Stud. Adv. 3 (2021), 100013.
[2] I. Ahmed, M. Ahmad, J.J.P.C. Rodrigues, G. Jeon and S. Din, A deep learning-based social distance monitoring
framework for COVID-19, Sustain. Cities Soc. 65 (2021).
[3] U.R. Alo, F.O. Nkwo, H.F. Nweke, I.I. Achi and H.A. Okemiri, Non-pharmaceutical interventions against COVID19 pandemic: Review of contact tracing and social distancing technologies, protocols, apps, security and open
research directions, Sensors 22 (2022), no. 1, 280.
[4] M.A. Ansari and D.K. Singh, Monitoring social distancing through human detection for preventing/reducing
COVID spread, Int. J. Inf. Technol. 13 (2021), no. 3, 1255–1264.
[5] K. Bhambani, T. Jain and K.A. Sultanpure, Real-time face mask and social distancing violation detection system
using YOLO, Proc. B-HTC 2020 - 1st IEEE Bangalore Humanit. Technol. Conf., 2020.
[6] A. Bochkovskiy, C.-Y. Wang and H.-Y.M. Liao, Yolov4: Optimal speed and accuracy of object detection, ArXiv
Prepr. arXiv2004.10934, (2020).
[7] N. Byrd and M. Bia lek, Your health vs. my liberty: Philosophical beliefs dominated reflection and identifiable victim
effects when predicting public health recommendation compliance during the COVID-19 pandemic, Cognition 212
(2021), 104649.
[8] P. Dollar, C. Wojek, B. Schiele and P. Perona, Pedestrian detection: An evaluation of the state of the art, IEEE
Trans. Pattern Anal. Mach. Intell. 34 (2011), no. 4, 743–761.
[9] M. Everingham, L. Van Gool, C.K.I. Williams, J. Winn and A. Zisserman, The pascal visual object classes (voc)
challenge, Int. J. Comput. Vis. 88 (2010), no. 2, 303–338.
[10] C.-Y. Fu, W. Liu, A. Ranga, A. Tyagi and A.C. Berg, Dssd: Deconvolutional single shot detector, arXiv Prepr.
arXiv1701.06659, (2017).
[11] R. Girshick, Fast R-CNN, in Proceedings of the IEEE international conference on computer vision, (2015), 1440–
1448.
[12] R. Girshick, J. Donahue, T. Darrell and J. Malik, Rich feature hierarchies for accurate object detection and
semantic segmentation, Proc. IEEE Conf. Comput. Vision Pattern Recog., 2014, pp. 580–587.
[13] S. Gupta, R. Kapil, G. Kanahasabai, S.S. Joshi and A.S. Joshi, SD-measure: A social distancing detector, Proc.
12th Int. Conf. Comput. Intell. Commun. Networks, CICN, 2020, pp. 306–311.
[14] M. Haris and A. Glowacz, Road object detection: a comparative study of deep learning-based algorithms, Electron.
10 (2021), no. 16, 1932.
[15] K. He, G. Gkioxari, P. Doll and R. Girshick, Mask R-CNN, Proc. IEEE Int. Conf. Comput. Vision, 2017, pp.
2961–2969.
[16] A.N. Ikwu, The impact of covid-19 pandemic on Africa’s healthcare system and psychosocial life, Eur. J. Nat. Sci.
Med. 4 (2021), no. 1, 39–50.
[17] M.M. Islam, F. Karray, R. Alhajj and J. Zeng, A review on deep learning techniques for the diagnosis of novel
coronavirus (COVID-19), IEEE Access 9 (2021), 30551–30572.
[18] Y. Jamtsho, P. Riyamongkol and R. Waranusast, Real-time license plate detection for non-helmeted motorcyclist
using YOLO, Ict Express 7 (2021), no. 1, 104–109.
[19] Y. Jing, Y. Ren, Y. Liu, D. Wang and L. Yu, Automatic extraction of damaged houses by earthquake based on
improved YOLOv5: A case study in Yangbi, Remote Sens. 14 (2022), no. 2, 382.
[20] R. Keniya and N. Mehendale, Real-time social distancing detector using socialdistancingNet-19 deep learning
network, Available at SSRN 3669311, (2020).
[21] M.Z. Khan, M.U.G. Khan, T. Saba, I. Razzak, A. Rehman and S.A. Bahaj, Hot-spot zone detection to tackle
Covid19 spread by fusing the traditional machine learning and deep learning approaches of computer vision, IEEEAccess 9 (2021), 100040–100049.
[22] G.S. Kumar and S.D. Shetty, Application development for mask detection and social distancing violation detection
using convolutional neural networks, ICEIS 2021–23rd Int. Conf. Enterprise Info. Syst. 1 (2021), 760–767.
[23] A. Kuznetsova, H. Rom, N. Alldrin, J. Uijlings, I. Krasin, J. Pont-Tuset, S. Kamali, S. Popov, M. Malloci, A.
Kolesnikov and T. Duerig, The open images dataset v4, Int. J. Comput. Vision 128 (2020), no. 7, 1956–1981.
[24] T.Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Doll´ar and C.L. Zitnick, Microsoft coco:
Common objects in context, Eur. Conf. Comput. Vision, 2014, pp. 740–755.
[25] W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.Y. Fu and A.C. Berg, Ssd: Single shot multibox detector,
Eur. Conf. Comput. Vision, 2016, pp. 21–37.
[26] R. Magoo, H. Singh, N. Jindal, N. Hooda and P.S. Rana, Deep learning-based bird eye view social distancing
monitoring using surveillance video for curbing the COVID-19 spread, Neural Comput. Appl. 33 (2021), no. 22,
15807–15814.
[27] E. Mbunge, Integrating emerging technologies into COVID-19 contact tracing: Opportunities, challenges and
pitfalls, Diabetes Metab. Syndr. Clin. Res. Rev. 14 (2020), no. 6, 1631–1636.
[28] E. Mbunge, Effects of COVID-19 in South African health system and society: An explanatory study, Diabetes
Metab. Syndr. Clin. Res. Rev. 14 (2020), no. 6, 1809–1814.
[29] E. Mbunge, B. Akinnuwesi, S.G. Fashoto, A.S. Metfula and P. Mashwama, A critical review of emerging technologies for tackling COVID-19 pandemic, Hum. Behav. Emerg. Technol. 3 (2021), no. 1, 25–39.
[30] E. Mbunge, S.G. Fashoto, B. Akinnuwesi, A. Metfula, S. Simelane and N. Ndumiso, Ethics for integrating emerging
technologies to contain COVID-19 in Zimbabwe, Hum. Behav. Emerg. Technol. 3 (2021), no. 5, 876–890.
[31] S. Meivel, N. Sindhwani, R. Anand, D. Pandey, A.A. Alnuaim, A.S. Altheneyan, M.Y. Jabarulla and M.E. Lelisho,
Mask detection and social distance identification using internet of things and faster R-CNN algorithm, Comput.
Intell. Neurosci. 2022 (2022).
[32] K. Ng, B.H. Poon, T.H. Kiat Puar, J.L. Shan Quah, W.J. Loh, Y.J. Wong, T.Y. Tan and J. Raghuram, COVID-19
and the risk to health care workers: A case report, Ann. Int. Med. 172 (2020), no. 11, 766–767.
[33] D. Pandit, S. Chougule, H. Fatepurwala, A. Kulkarni, N. Kakade and A. Sundge, Generalized method to validate
social distancing using median angle proximity methodology, Proc. 3rd Int. Conf. Intell. Sustain. Syst. ICISS,
2020, pp. 279–284.
[34] 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) 1–10.
[35] J. Qin and N. Xu, Reaserch and implementation of social distancing monitoring technology based on SSD, Procedia
Comput. Sci. 183 (2021), 768–775.
[36] S. Rab, M. Javaid, A. Haleem and R. Vaishya, Face masks are new normal after COVID-19 pandemic, Diabetes
Metab. Syndr. Clin. Res. Rev. 14 (2020), no. 6, 1617–1619.
[37] A. Rahim, A. Maqbool and T. Rana, Monitoring social distancing under various low light conditions with deep
learning and a single motionless time of flight camera, PLoS One 16 (2021), no. 2, 1–19.
[38] J. Redmon, S. Divvala, R. Girshick and A. Farhadi, You only look once: Unified, real-time object detection, Proc.
IEEE Conf. Comput. Vision Pattern Recogn., 2016, pp. 779–788.
[39] J. Redmon and A. Farhadi, YOLO9000: Better, faster, stronger, Proc. IEEE Conf. Comput. Vision Pattern
Recogn., 2017, pp. 7263–7271.
[40] J. Redmon and A. Farhadi, Yolov3: An incremental improvement, arXiv Prepr. arXiv1804.02767, (2018).
[41] H.S. Rekha, H.S. Behera, J. Nayak and B. Naik, Deep learning for covid-19 prognosis: A systematic review, Intell.
Comput. Control Commun. (2021), 667–687.
[42] S. Ren, K. He, R. Girshick and J. Sun, Faster R-CNN: Towards real-time object detection with region proposal
networks, Adv. Neural Inf. Process. Syst. 28 (2015).[43] B. Roy, S. Nandy, D. Ghosh, D. Dutta, P. Biswas and T. Das, MOXA: A deep learning based unmanned approach
for real-time monitoring of people wearing medical masks, Trans. Indian Natl. Acad. Eng. 5 (2020), no. 3, 509–518.
[44] O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein
and A.C. Berg, Imagenet large scale visual recognition challenge, Int. J. Comput. Vis. 115 (2015), no. 3, 211–252.
[45] S. Saponara, A. Elhanashi and A. Gagliardi, Implementing a real-time, AI-based, people detection and social
distancing measuring system for Covid-1 9, J. Real-Time Image Process. 18 (2021), no. 6, 1937–1947.
[46] Z. Shao, G. Cheng, J. Ma, Z. Wang, J. Wang and D. Li, Real-time and accurate UAV pedestrian detection for
social distancing monitoring in COVID-19 pandemic, IEEE Trans. Multimed. 24 (2021), 1–16.
[47] S.W. Sim, K.S.P. Moey and N.C. Tan, The use of facemasks to prevent respiratory infection: A literature review
in the context of the health belief model, Singapore Med. J. 55 (2014), no. 3, 160.
[48] M. Sriharsha, S. Jindam, A. Gandla and L.S. Allani, Social distancing detector using deep learning, Int. J. Recent
Technol. Eng. 10 (2022), no. 5, 146–149.
[49] S. Srinivasan, R. Rujula Singh, R.R. Biradar and S.A. Revathi, COVID-19 monitoring system using social distancing and face mask detection on surveillance video datasets, Int. Conf. Emerg. Smart Comput. Informatics,
ESCI, 2021, pp. 449–455.
[50] Y. Su, D. Li and X. Chen, Lung nodule detection based on faster R-CNN framework, Comput. Methods Programs
Biomed. 200 (2021), 105866.
[51] R. Vaishya, M. Javaid, I.H. Khan and A. Haleem, Artificial intelligence (AI) applications for COVID-19 pandemic,
Diabetes Metab. Syndr. Clin. Res. Rev. 14 (2020), no. 4, 337–339.
[52] D. Yang, E. Yurtsever, V. Renganathan, K.A. Redmill and U. ¨ Ozg¨uner, ¨ A vision-based social distancing and
critical density detection system for COVID-19, Sensors (Basel) 21 (2021), no. 13.
[53] C. Yu, Z. Hu, R. Li, X. Xia, Y. Zhao, X. Fan and Y. Bai, Segmentation and density statistics of mariculture cages
from remote sensing images using mask R-CNN, Inf. Process. Agric. In Press, (2021).
[54] Z. Zhang, Y. Li, W. Wu, H. Chen, L. Cheng and S. Wang, Tumor detection using deep learning method in
automated breast ultrasound, Biomed. Signal Process. Control 68 (2021), 102677.
[55] P. Zhu, L. Wen, X. Bian, H. Ling and Q. Hu, Vision meets drones: A challenge, arXiv Prepr. arXiv1804.07437,
(2018).
)
Volume 13, Issue 2
July 2022Pages 2865-2880
- Receive Date: 11 February 2022
- Revise Date: 18 March 2022
- Accept Date: 08 April 2022