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Social distance in object detection: Survey based on cutting-edge deep learning approach | ||
| International Journal of Nonlinear Analysis and Applications | ||
| مقاله 230، دوره 13، شماره 2، مهر 2022، صفحه 2865-2880 اصل مقاله (604.81 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2022.27431.3598 | ||
| نویسندگان | ||
| Hiyam Hashem Saeed* ؛ Abdulbasit Alazzawi | ||
| Department of Computer Science, College of Science, University of Diyala, Baqubah, Iraq | ||
| تاریخ دریافت: 22 بهمن 1400، تاریخ بازنگری: 27 اسفند 1400، تاریخ پذیرش: 19 فروردین 1401 | ||
| چکیده | ||
| 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. | ||
| کلیدواژهها | ||
| Deep learning؛ COVID19؛ Convolutional neural network؛ Social distancing؛ Object detection | ||
| مراجع | ||
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[1] M. Abboah-Offei, Y. Salifu, B. Adewale, J. Bayuo, R. Ofosu-Poku and E.B.A. Opare-Lokko, A rapid review of 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 COVID-19 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, ArXivPrepr. 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, Research 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. Ozguner, 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). | ||
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