Computer Vision-Based Affordable High-Density Traffic Monitoring System

Arafat, Mohammed Yasin

IUT Repository Home
→
Electrical and Electronics Engineering (EEE)
→
Thesis
→
Graduate
→
2023
→
View Item

dc.contributor.author	Arafat, Mohammed Yasin
dc.date.accessioned	2024-01-15T06:42:09Z
dc.date.available	2024-01-15T06:42:09Z
dc.date.issued	2023-06-30
dc.identifier.citation	[1] M. S. H. Onim, M. I. Akash, M. Haque and R. I. Hafiz, "Traffic Surveillance using Vehicle License Plate Detection and Recognition in Bangladesh," 2020 11th International Conference on Electrical and Computer Engineering (ICECE), 2020, pp. 121-124, doi: 10.1109/ICECE51571.2020.9393109. [2] Huansheng Song, Haoxiang Li ang, Huaiyu Li, Zhe Dai and Xu Yum, “Vision-based vehicle detection and counting system using deep learning in highway scenes”, in Journal of Song European Transport Research Review (2019), pp. 1 [3] https://www.nytimes.com/2016/09/23/t-magazine/travel/dhaka-bangladesh-traffic.html [4] J. Lin et al., "From computer vision to short text understanding: Applying similar approaches into different disciplines," in Journal of Intelligent and Converged Networks, vol. 3, no. 2, pp. 161-172, June 2022, doi: 10.23919/ICN.2022.0010. [5] K. M. Pai, K. B. A. Shenoy and M. M. M. Pai, "A Computer Vision Based Behavioral Study and Fish Counting in a Controlled Environment," in Journal IEEE Access, vol. 10, pp. 87778- 87786, 2022, doi: 10.1109/ACCESS.2022.3197887 [6] A. S. Mohammed Shariff, R. Bhatia, R. Kuma and S. Jha, "Vehicle Number Plate Detection Using Python and Open CV," 2021 International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), 2021, pp. 525-529, doi: 10.1109/ICACITE51222.2021.9404556. [7] A. Kumar and S. P. Panda, "A Survey: How Python Pitches in IT-World," 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon), 2019, pp. 248-251, doi: 10.1109/COMITCon.2019.8862251. [8] A. Zaarane, I. Slimani, W. Al Okaishi, I. Atouf and A. Hamdoun, "An automated night-time vehicle detection system for driving assistance based on cross-correlation," 2019 International Conference on Systems of Collaboration Big Data, Internet of Things & Security (SysCoBIoTS), 2019, pp. 1-5, doi: 10.1109/SysCoBIoTS48768.2019.9028038. [9] X. Zhang, B. Story and D. Rajan, "Night Time Vehicle Detection and Tracking by Fusing Vehicle Parts From Multiple Cameras," in Journal IEEE Transactions on Intelligent 60 Transportation Systems, vol. 23, no. 7, pp. 8136-8156, July 2022, doi: 10.1109/TITS.2021.3076406. [10] H. Kuang, L. Chen, F. Gu, J. Chen, L. Chan and H. Yan, "Combining Region-of-Interest Extraction and Image Enhancement for Nighttime Vehicle Detection," in IEEE Intelligent Systems, vol. 31, no. 3, pp. 57-65, May-June 2016, doi: 10.1109/MIS.2016.17 [11] X. -Z. Chen, K. -K. Liao, Y. -L. Chen, C. -W. Yu and C. Wang, "A vision-based nighttime surrounding vehicle detection system," 2018 7th International Symposium on Next Generation Electronics (ISNE), 2018, pp. 1-3, doi: 10.1109/ISNE.2018.8394717. [12] L. Ewecker, E. Asan and S. Roos, "Detecting vehicles in the dark in urban environments - A human benchmark," 2022 IEEE Intelligent Vehicles Symposium (IV), 2022, pp. 1145-1151, doi: 10.1109/IV51971.2022.9827013. [13] G. Liu et al., "Smart Traffic Monitoring System Using Computer Vision and Edge Computing," in Journal IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 8, pp. 12027-12038, Aug. 2022, doi: 10.1109/TITS.2021.3109481. [14] Vedant Singh, "Intelligent Traffic Management System," International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-8 Issue-3, September 2019, pp.4 [15] Lewandowski, Marcin & Płaczek, Bartlomiej & Bernaś, Marcin & Szymała, Piotr. (2018). Road Traffic Monitoring System Based on Mobile Devices and Bluetooth Low Energy Beacons. In journal of Wireless Communications and Mobile Computing. 2018. 1-12. 10.1155/2018/3251598. [16] Dr. Suwarna Gothane, “A Practice for Object Detection Using YOLO Algorithm”, 2021 International Journal of Scientific Research in Computer Science, Engineering and Information Technology, pp. 1,4. [17] https://www.kyosan.co.jp/english/product/traffic04.html [18] https://appen.com/blog/computer-vision-vs-machine-vision 61 [19] https://www.datarobot.com/blog/introduction-to-computer-vision-what-it-is-and-how-it works [20] N Dewantoro, “YOLO Algorithm Accuracy Analysis in Detecting Amount of Vehicles at the Intersection,” IOP Conf. Series: Earth and Environmental Science 426 (2020) 012164, doi:10.1088/1755-1315/426/1/012164 [21] Vedant Singh, “Intelligent Traffic Management System,” International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-8 Issue-3, September 2019 [22] Redmon, Joseph & Farhadi, Ali. (2018), “YOLOv3: An Incremental Improvement,” Available: arXiv:1804.02767v1 [cs.CV]. [23] Q. Xu, R. Lin, H. Yue, H. Huang, Y. Yang and Z. Yao, "Research on Small Target Detection in Driving Scenarios Based on Improved Yolo Network," in Journal of IEEE Access, vol. 8, pp. 27574-27583, 2020, doi: 10.1109/ACCESS.2020.2966328. [24] W. Dong, Z. Yang, W. Ling, Z. Yonghui, L. Ting and Q. Xiaoliang, "Research on vehicle detection algorithm based on convolutional neural network and combining color and depth images," 2019 2nd International Conference on Information Systems and Computer Aided Education (ICISCAE), 2019, pp. 274-277, doi: 10.1109/ICISCAE48440.2019.221634. [25] J. Hu, X. Gao, H. Wu and S. Gao, "Detection of Workers Without the Helments in Videos Based on YOLO V3," 2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), 2019, pp. 1-4, doi: 10.1109/CISP BMEI48845.2019.8966045. [26] Y. Hu, X. Wu, G. Zheng and X. Liu, "Object Detection of UAV for Anti-UAV Based on Improved YOLO v3," 2019 Chinese Control Conference (CCC), 2019, pp. 8386-8390, doi: 10.23919/ChiCC.2019.8865525. 62 [27] M. Nie and C. Wang, "Pavement Crack Detection based on yolo v3," 2019 2nd International Conference on Safety Produce Informatization (IICSPI), 2019, pp. 327-330, doi: 10.1109/IICSPI48186.2019.9095956. [28] J. Liao and J. Zou, "Smoking target detection based on Yolo V3," 2020 5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE), 2020, pp. 2241- 2244, doi: 10.1109/ICMCCE51767.2020.00486. [29] R. B. Diwate, A. Zagade, M. R. Khodaskar and V. R. Dange, "Optimization in Object Detection Model using YOLO.v3," 2022 International Conference on Emerging Smart Computing and Informatics (ESCI), 2022, pp. 1-4, doi: 10.1109/ESCI53509.2022.9758381. [30] Y. Li, Q. Wang and R. Liu, "Research on YOLOv3 pedestrian detection algorithm based on channel attention mechanism," 2021 IEEE International Conference on Computer Science, Electronic Information Engineering and Intelligent Control Technology (CEI), 2021, pp. 229- 232, doi: 10.1109/CEI52496.2021.9574546. [31] Handalage, Upulie & Kuganandamurthy, Lakshini. (2021). “Real-Time Object Detection Using YOLO: A Review.” 10.13140/RG.2.2.24367.66723. [32] W. Fang, L. Wang and P. Ren, "Tinier-YOLO: A Real-Time Object Detection Method for Constrained Environments," in Journal IEEE Access, vol. 8, pp. 1935-1944, 2020, doi: 10.1109/ACCESS.2019.2961959. [33] M. Mahmud, M. S. Islam, A. Ahmed, M. Younis, and F.-S. Choa "Cross-Medium Photoacoustic Communications: Challenges, and State of the Art," Sensors, 22(11), pp. 4224, June 2022. [34] Y. Miao, F. Liu, T. Hou, L. Liu and Y. Liu, "A Nighttime Vehicle Detection Method Based on YOLO v3," 2020 Chinese Automation Congress (CAC), 2020, pp. 6617-6621, doi: 10.1109/CAC51589.2020.9326819	en_US
dc.identifier.uri	http://hdl.handle.net/123456789/2019
dc.description	Supervised by Prof. Dr. Md. Fokhrul Islam , Department of Electrical and Electronics Engineering (EEE) Islamic University of Technology (IUT) Board Bazar, Gazipur-1704, Bangladesh	en_US
dc.description.abstract	Vehicle detection and traffic monitoring system play a significant role in many areas of transportation infrastructure and to reduce traffic congestion. In this research, an affordable computer vision-based amazingly simple method has been proposed to detect and track vehicles, and also monitor high traffic density using CCTV cameras. Traffic monitoring system’s infrastructure is expensive to build and implement. An affordable traffic monitoring system has been proposed which when implemented will be able to autonomize traffic monitoring system consequently reduce traffic congestion. Still image and traffic footage dataset were collected and input into the model. Vehicles were detected by YOLOv3 algorithm which was modified to detect 5 classes of vehicles only. Traffic density was estimated with respect to time and the whole model was implemented in Raspberry Pi. A series of experiments were conducted to validate the accuracy and affordability of the model. Highest model accuracy obtained from vehicle detection from still image was 92%, vehicle detection from traffic footage was 88%, traffic density estimation was 88%. When compared with existing model, the proposed model was 3 times more affordable. Variations in lighting and angles of camera position in real life can affect the vehicle detection and identification process which will provide further scopes of work. This research was aimed to contribute a little to pave the pathways which will help traffic monitoring and ultimately reduce traffic congestion	en_US
dc.language.iso	en	en_US
dc.publisher	Department of Electrical and Elecrtonics Engineering(EEE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh	en_US
dc.title	Computer Vision-Based Affordable High-Density Traffic Monitoring System	en_US
dc.type	Thesis	en_US