Deep neural network-based physical distancing monitoring system with tensorRT optimization

(1) * Edi Kurniawan Mail (Research Center for Photonics, National Research and Innovation Agency (BRIN), Tangerang Selatan, Indonesia)
(2) Hendra Adinanta Mail (Research Center for Hydrodinamics Technology, National Research and Innovation Agency (BRIN), Surabaya, Indonesia)
(3) Suryadi Suryadi Mail (Research Center for Photonics, National Research and Innovation Agency (BRIN), Tangerang Selatan, Indonesia)
(4) Bernadus Herdi Sirenden Mail (Research Center for Electronics, National Research and Innovation Agency (BRIN), Tangerang Selatan, Indonesia)
(5) Rini Khamimatul Ula Mail (Research Center for Photonics, National Research and Innovation Agency (BRIN), Tangerang Selatan, Indonesia)
(6) Hari Pratomo Mail (Research Center for Photonics, National Research and Innovation Agency (BRIN), Tangerang Selatan, Indonesia)
(7) Purwowibowo Purwowibowo Mail (Research Center for Photonics, National Research and Innovation Agency (BRIN), Tangerang Selatan, Indonesia)
(8) Jalu Ahmad Prakosa Mail (Research Center for Photonics, National Research and Innovation Agency (BRIN), Tangerang Selatan, Indonesia)
*corresponding author

Abstract


During the COVID-19 pandemic, physical distancing (PD) is highly recommended to stop the transmission of the virus. PD practices are challenging due to humans' nature as social creatures and the difficulty in estimating the distance from other people. Therefore, some technological aspects are required to monitor PD practices, where one of them is computer vision-based approach. Hence, deep learning-based computer vision is utilized to automatically detect human objects in the video surveillance. In this work, we focus on the performance study of deep learning-based object detector with Tensor RT optimization for the application of physical distancing monitoring system. Deep learning-based object detection is employed to discover people in the crowd. Once the objects have been detected, then the distances between objects can be calculated to determine whether those objects violate physical distancing or not. This work presents the physical distancing monitoring system using a deep neural network. The optimization process is based on TensorRT executed on Graphical Processing Unit (GPU) and Computer Unified Device Architecture (CUDA) platform. This research evaluates the inferencing speed of the well-known object detection model You-Only-Look-Once (YOLO) run on two different Artificial Intelligence (AI) machines. Two different systems-based on Jetson platform are developed as portable devices functioning as PD monitoring stations. The results show that the inferencing speed in regard to Frame-Per-Second (FPS) increases up to 9 times of the non-optimized ones, while maintaining the detection accuracies.

   

DOI

https://doi.org/10.26555/ijain.v8i2.824
      

Article metrics

Abstract views : 263 | PDF views : 93

   

Cite

   

Full Text

Download

References


[1] N. R. Jones, Z. U. Qureshi, R. J. Temple, J. P. J. Larwood, T. Greenhalgh, and L. Bourouiba, “Two metres or one: what is the evidence for physical distancing in covid-19?,” BMJ, vol. 370, p. m3223, Aug. 2020, doi: 10.1136/bmj.m3223.

[2] D. Bergman, C. Bethell, N. Gombojav, S. Hassink, and K. C. Stange, “Physical Distancing With Social Connectedness,” Ann. Fam. Med., vol. 18, no. 3, pp. 272–277, May 2020, doi: 10.1370/afm.2538.

[3] M. L. Parra Gordo, G. Buitrago Weiland, M. Grau García, and G. Arenaza Choperena, “Radiologic aspects of COVID-19 pneumonia: Outcomes and thoracic complications,” Radiol. (English Ed., vol. 63, no. 1, pp. 74–88, Jan. 2021, doi: 10.1016/j.rxeng.2020.11.002.

[4] N. B. Masters et al., “Social distancing in response to the novel coronavirus (COVID-19) in the United States,” PLoS One, vol. 15, no. 9, pp. 1–12, Sep. 2020, doi: 10.1371/journal.pone.0239025.

[5] I. Kuitunen, M. Artama, L. Mäkelä, K. Backman, T. Heiskanen-Kosma, and M. Renko, “Effect of Social Distancing Due to the COVID-19 Pandemic on the Incidence of Viral Respiratory Tract Infections in Children in Finland During Early 2020,” Pediatr. Infect. Dis. J., vol. 39, no. 12, pp. e423–e427, Dec. 2020, doi: 10.1097/INF.0000000000002845.

[6] C. T. Nguyen et al., “A Comprehensive Survey of Enabling and Emerging Technologies for Social Distancing—Part I: Fundamentals and Enabling Technologies,” IEEE Access, vol. 8, pp. 153479–153507, 2020, doi: 10.1109/ACCESS.2020.3018140.

[7] C. T. Nguyen et al., “A Comprehensive Survey of Enabling and Emerging Technologies for Social Distancing—Part II: Emerging Technologies and Open Issues,” IEEE Access, vol. 8, pp. 154209–154236, 2020, doi: 10.1109/ACCESS.2020.3018124.

[8] S. Saponara, A. Elhanashi, and A. Gagliardi, “Implementing a real-time, AI-based, people detection and social distancing measuring system for Covid-19,” J. Real-Time Image Process., vol. 18, no. 6, pp. 1937–1947, Dec. 2021, doi: 10.1007/s11554-021-01070-6.

[9] S. Suryadi, E. Kurniawan, H. Adinanta, B. H. Sirenden, J. A. Prakosa, and P. Purwowibowo, “On the Comparison of Social Distancing Violation Detectors with Graphical Processing Unit Support,” in 2020 International Conference on Radar, Antenna, Microwave, Electronics, and Telecommunications (ICRAMET), 2020, pp. 337–342, doi: 10.1109/ICRAMET51080.2020.9298574.

[10] M. Rezaei and M. Azarmi, “DeepSOCIAL: Social Distancing Monitoring and Infection Risk Assessment in COVID-19 Pandemic,” Appl. Sci., vol. 10, no. 21, pp. 1–29, Oct. 2020, doi: 10.3390/app10217514.

[11] 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, vol. 16, no. 2, pp. 1–19, Feb. 2021, doi: 10.1371/journal.pone.0247440.

[12] E. Jeong, J. Kim, and S. Ha, “TensorRT-based Framework and Optimization Methodology for Deep Learning Inference on Jetson Boards,” ACM Trans. Embed. Comput. Syst., pp. 1–26, Jan. 2022, doi: 10.1145/3508391.

[13] D.-J. Shin and J.-J. Kim, “A Deep Learning Framework Performance Evaluation to Use YOLO in Nvidia Jetson Platform,” Appl. Sci., vol. 12, no. 8, pp. 1–19, Apr. 2022, doi: 10.3390/app12083734.

[14] S. Mittal, “A Survey on optimized implementation of deep learning models on the NVIDIA Jetson platform,” J. Syst. Archit., vol. 97, no. January, pp. 428–442, Aug. 2019, doi: 10.1016/j.sysarc.2019.01.011.

[15] Y. Chen, B. Zheng, Z. Zhang, Q. Wang, C. Shen, and Q. Zhang, “Deep Learning on Mobile and Embedded Devices,” ACM Comput. Surv., vol. 53, no. 4, pp. 1–37, Jul. 2021, doi: 10.1145/3398209.

[16] A. Osipov et al., “Identification and Classification of Mechanical Damage During Continuous Harvesting of Root Crops Using Computer Vision Methods,” IEEE Access, vol. 10, pp. 28885–28894, 2022, doi: 10.1109/ACCESS.2022.3157619.

[17] Z. Lingxin, S. Junkai, and Z. Baijie, “A review of the research and application of deep learning-based computer vision in structural damage detection,” Earthq. Eng. Eng. Vib., vol. 21, no. 1, pp. 1–21, Jan. 2022, doi: 10.1007/s11803-022-2074-7.

[18] B. Kim, N. Yuvaraj, H. W. Park, K. R. S. Preethaa, R. A. Pandian, and D.-E. Lee, “Investigation of steel frame damage based on computer vision and deep learning,” Autom. Constr., vol. 132, no. September, p. 103941, Dec. 2021, doi: 10.1016/j.autcon.2021.103941.

[19] D. Wang, “Intelligent Detection of Vehicle Driving Safety Based on Deep Learning,” Wirel. Commun. Mob. Comput., vol. 2022, pp. 1–11, Jun. 2022, doi: 10.1155/2022/1095524.

[20] J. Ni, Y. Chen, Y. Chen, J. Zhu, D. Ali, and W. Cao, “A Survey on Theories and Applications for Self-Driving Cars Based on Deep Learning Methods,” Appl. Sci., vol. 10, no. 8, pp. 1–29, Apr. 2020, doi: 10.3390/app10082749.

[21] K. Muhammad, A. Ullah, J. Lloret, J. Del Ser, and V. H. C. de Albuquerque, “Deep Learning for Safe Autonomous Driving: Current Challenges and Future Directions,” IEEE Trans. Intell. Transp. Syst., vol. 22, no. 7, pp. 4316–4336, Jul. 2021, doi: 10.1109/TITS.2020.3032227.

[22] R. Ayachi, M. Afif, Y. Said, and M. Atri, “Traffic Signs Detection for Real-World Application of an Advanced Driving Assisting System Using Deep Learning,” Neural Process. Lett., vol. 51, no. 1, pp. 837–851, Feb. 2020, doi: 10.1007/s11063-019-10115-8.

[23] Z. Wu, X. Wang, and C. Chen, “Research on Lightweight Infrared Pedestrian Detection Model Algorithm for Embedded Platform,” Secur. Commun. Networks, vol. 2021, pp. 1–7, Nov. 2021, doi: 10.1155/2021/1549772.

[24] H. Wu, Y. Hua, H. Zou, and G. Ke, “A lightweight network for vehicle detection based on embedded system,” J. Supercomput., pp. 1-16., Jun. 2022, doi: 10.1007/s11227-022-04596-z.

[25] A. K. A. Al Ghanadi, W. Mateen, and R. G. Ramaswamy, Ilias Maglogiannis Lazaros Iliadis Applications. Springer, Cham, 2020. Available at: Google Scholar.

[26] H. Adinanta, E. Kurniawan, Suryadi, and J. A. Prakosa, “Physical Distancing Monitoring with Background Subtraction Methods,” in 2020 International Conference on Radar, Antenna, Microwave, Electronics, and Telecommunications (ICRAMET), 2020, pp. 45–50, doi: 10.1109/ICRAMET51080.2020.9298687.

[27] S. Lu, B. Wang, H. Wang, L. Chen, M. Linjian, and X. Zhang, “A real-time object detection algorithm for video,” Comput. Electr. Eng., vol. 77, pp. 398–408, Jul. 2019, doi: 10.1016/j.compeleceng.2019.05.009.

[28] L. Jiao et al., “A Survey of Deep Learning-Based Object Detection,” IEEE Access, vol. 7, pp. 128837–128868, 2019, doi: 10.1109/ACCESS.2019.2939201.

[29] L. Zhao and S. Li, “Object Detection Algorithm Based on Improved YOLOv3,” Electronics, vol. 9, no. 3, pp. 1–11, Mar. 2020, doi: 10.3390/electronics9030537.

[30] A. M. Roy, R. Bose, and J. Bhaduri, “A fast accurate fine-grain object detection model based on YOLOv4 deep neural network,” Neural Comput. Appl., vol. 34, no. 5, pp. 3895–3921, Mar. 2022, doi: 10.1007/s00521-021-06651-x.

[31] F. Abdurahman, K. A. Fante, and M. Aliy, “Malaria parasite detection in thick blood smear microscopic images using modified YOLOV3 and YOLOV4 models,” BMC Bioinformatics, vol. 22, no. 1, pp. 1–17, Dec. 2021, doi: 10.1186/s12859-021-04036-4.

[32] C.-Y. Wang, H.-Y. Mark Liao, Y.-H. Wu, P.-Y. Chen, J.-W. Hsieh, and I.-H. Yeh, “CSPNet: A New Backbone that can Enhance Learning Capability of CNN,” in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2020, vol. 2020-June, pp. 1571–1580, doi: 10.1109/CVPRW50498.2020.00203.

[33] NVIDIA, “NVIDIA TensorRT Documentation,” Nvidia Accelerated Computing, 2022. [Online]. Available: docs.nvidia.com. [Accessed: 20-Jun-2022].

[34] A. Harvey and J. LaPlace, “Researchers Gone Wild,” in Practicing Sovereignty, transcript Verlag, 2021, pp. 289–310. doi: 10.1515/9783839457603-016




Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

___________________________________________________________
International Journal of Advances in Intelligent Informatics
ISSN 2442-6571  (print) | 2548-3161 (online)
Organized by UAD and ASCEE Computer Society
Published by Universitas Ahmad Dahlan
W: http://ijain.org
E: info@ijain.org (paper handling issues)
   andri.pranolo.id@ieee.org (publication issues)

View IJAIN Stats

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0