Detection of Traffic Rule Violations Using Machine Learning: An Analytical Review
Keywords:Image Processing, Automated Traffic Monitoring, Traffic Violation Detection, Machine Learning, Artificial Intelligence
This research paper focuses on current and previous efforts to detect traffic rule violations. So far, some remarkable works have been discovered, and many approaches for detecting traffic rule violations have been introduced from the current situation. Hence, machine learning has been the main target to detect traffic rule violations. A summary of the frameworks and methods that have been used to solve this problem so far is also provided in this study. This study has been divided into two parts. In the first part, the recent works on traffic rule violations have been portrayed. Moreover, the algorithms and frameworks that have been used so far and major works on violation detection using machine learning can be found in this section. In the second part, this study summarizes a brief discussion based on the image quality, camera resolution, device performance, and accuracy level of the works, as well as the algorithms and frameworks that have been used to conduct the detection of traffic rule violation problems using machine learning.
J. Islam. “Many of Dhaka’s traffic police boxes stand in pedestrians’ way.” The Business Standard. https://www.tbsnews.net/bangladesh/many-dhakas-traffic-police-boxes-stand-pedestrians-way-465186#:~:text=According%20to%20the%20Dhaka%20Metropolitan,the%20Dhaka%20South%20City%20Corporation (accessed Dec. 30, 2022).
I. S. Chowdhury. “Bangladesh road accidents leave 17 people dead every day.” The Daily Observer. https://www.observerbd.com/news.php?id=389274 (accessed Dec. 30, 2022).
D. BONNICI. “How many cars are there in the world?” Which Car. https://www.whichcar.com.au/news/how-many-cars-are-there-in-the-world (accessed Dec. 30, 2022).
“Motor Vehicle Registered: Bangladesh: Total” CEIC Data. https://www.ceicdata.com/en/bangladesh/motor-vehicle-registered/motor-vehicle-registered-bangladesh-total#:~:text=Motor%20Vehicle%20Registered%3A%20Bangladesh%3A%20Total%20data%20was%20reported,at%20445%2C030.000%20Unit%20in%202021 (accessed Dec. 30, 2022).
F. Mahmud. “Eid holidays in Bangladesh saw record road accident deaths: Group” Aljazeera. https://www.aljazeera.com/news/2022/7/24/eid-holidays-in-bangladesh-saw-record-road-accident-deaths-group (accessed Dec. 29, 2022).
Michalopoulos, P.G., 1991. Vehicle detection video through image processing: the autoscope system. IEEE Transactions on vehicular technology, 40(1), pp.21-29.
Pavlidis, I., Morellas, V. and Papanikolopoulos, N., 2000. A vehicle occupant counting system based on near-infrared phenomenology and fuzzy neural classification. IEEE Transactions on intelligent transportation systems, 1(2), pp.72-85.
Li, Q., Cheng, H., Zhou, Y. and Huo, G., 2015. Road vehicle monitoring system based on intelligent visual internet of things. Journal of Sensors, 2015.
Fuad, M., Arnob, F.A., Nizam, A.T. and Islam, M., 2020. A Novel Traffic System for Detecting Lane-Based Rule Violation. Annals of Emerging Technologies in Computing (AETiC), Print ISSN, pp.2516-0281.
Arnob, F.A., Fuad, A., Nizam, A.T., Barua, S., Choudhury, A.A. and Islam, M., 2020, February. An intelligent traffic system for detecting lane based rule violation. In 2019 International Conference on Advances in the Emerging Computing Technologies (AECT) (pp. 1-6). IEEE.
Aly, H., Basalamah, A. and Youssef, M., 2015, March. Lanequest: An accurate and energy-efficient lane detection system. In 2015 IEEE International Conference on Pervasive Computing and Communications (PerCom) (pp. 163-171). IEEE.
Sparbert, J., Dietmayer, K. and Streller, D., 2001, August. Lane detection and street type classification using laser range images. In ITSC 2001. 2001 IEEE Intelligent Transportation Systems. Proceedings (Cat. No. 01TH8585) (pp. 454-459). IEEE.
Aziz, M.V.G., Prihatmanto, A.S. and Hindersah, H., 2017, October. Implementation of lane detection algorithm for self-driving car on toll road cipularang using Python language. In 2017 4th international conference on electric vehicular technology (ICEVT) (pp. 144-148). IEEE.
Fang, Z., Hwang, J.N. and Huang, S.C., 2018. Advanced Visual Analyses for Smart and Autonomous Vehicles. Advances in Multimedia, 2018.
Ye, Y.Y., Hao, X.L. and Chen, H.J., 2018. Lane detection method based on lane structural analysis and CNNs. IET Intelligent Transport Systems, 12(6), pp.513-520.
Kim, Z., 2008. Robust lane detection and tracking in challenging scenarios. IEEE Transactions on intelligent transportation systems, 9(1), pp.16-26.
Sridharamurthy, K., Govinda, A.P., Gopal, J.D. and Varaprasad, G., 2016. Violation detection method for vehicular ad hoc networking. Security and Communication Networks, 9(3), pp.201-207.
Lin, H.Y., Li, K.J. and Chang, C.H., 2008. Vehicle speed detection from a single motion blurred image. Image and Vision Computing, 26(10), pp.1327-1337.
Arash, G.R., Abbas, D. and Mohamed, R.K., 2010. Vehicle speed detection in video image sequences using CVS method. International Journal of Physical Sciences, 5(17), pp.2555-2563.
Shim, K.S., in Park, N., Kim, J.H., Jeon, O.Y. and Lee, H., 2021. Vehicle Speed Measurement Methodology Robust to Playback Speed-Manipulated Video File. IEEE Access, 9, pp.132862-132874.
Grents, A., Varkentin, V. and Goryaev, N., 2020. Determining vehicle speed based on video using convolutional neural network. Transportation Research Procedia, 50, pp.192-200.
Luvizon, D.C., Nassu, B.T. and Minetto, R., 2016. A video-based system for vehicle speed measurement in urban roadways. IEEE Transactions on Intelligent Transportation Systems, 18(6), pp.1393-1404.
Dahl, M. and Javadi, S., 2019. Analytical modeling for a video-based vehicle speed measurement framework. Sensors, 20(1), p.160.
Uy, A.C.P., Bedruz, R.A., Quiros, A.R., Bandala, A. and Dadios, E.P., 2015, December. Machine vision for traffic violation detection system through genetic algorithm. In 2015 International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM) (pp. 1-7). IEEE.
Ibadov, S.R., Kalmykov, B.Y., Ibadov, R.R. and Sizyakin, R.A., 2019. Method of automated detection of traffic violation with a convolutional neural network. In EPJ Web of Conferences (Vol. 224, p. 04004). EDP Sciences
Agarwal, P., Chopra, K., Kashif, M. and Kumari, V., 2018. Implementing ALPR for detection of traffic violations: a step towards sustainability. Procedia Computer Science, 132, pp.738-743.
Wen, Y., Lu, Y., Yan, J., Zhou, Z., von Deneen, K.M. and Shi, P., 2011. An algorithm for license plate recognition applied to intelligent transportation system. IEEE Transactions on intelligent transportation systems, 12(3), pp.830-845.
Franklin, R.J., 2020, June. Traffic signal violation detection using artificial intelligence and deep learning. In 2020 5th International Conference on Communication and Electronics Systems (ICCES) (pp. 839-844). IEEE.
Harper, J.G., 1991. Traffic violation detection and deterrence: implications for automatic policing. Applied Ergonomics, 22(3), pp.189-197.
Roy, S., Bose, R. and Sarddar, D., 2015. A fog-based dss model for driving rule violation monitoring framework on the internet of things. International Journal of Advanced Science and Technology, 82, pp.23-32.
Aliane, N., Fernandez, J., Mata, M. and Bemposta, S., 2014. A system for traffic violation detection. Sensors, 14(11), pp.22113-22127.
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Copyright (c) 2023 Niloy Kanti Paul, Dipanwita Saha, Kaushik Biswas, Tanvir Ahmed, Rifath Mahmud
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