VEHICLE DETECTION, TRACKING, COUNTING AND CLASSIFICATION USING DEEP LEARNING

Authors

  • O. T. Ajayi Department of Surveying and Geoinformatics, College of Environmental Sciences, Bells University of Technology, Ota, Ogun State, Nigeria https://orcid.org/0009-0008-2284-7727
  • J. O. Olusina Department of Surveying and Geoinformatics, Faculty of Engineering, University of Lagos, Lagos, Nigeria

DOI:

https://doi.org/10.4314/njt.v43i4.13

Keywords:

Deep Learning, Computer vision, Vehicle counting system, Transportation management system (TMS), Real-time video analysis

Abstract

This paper explores vehicle counting system, which is a crucial component of building a robust transportation management system (TMS) that proffers solution to various challenges facing transportation systems in modern cities around the world. Although there are existing approaches such as the manual vehicular counting and hardware-based systems but they are plagued with various limitations such as being intrusive on roads, difficult to scale and also very expensive to maintain. Hence, there are not enough viable solutions to the current complex and diverse traffic challenges. This paper focused on the development of a vehicle counting system designed to capture and read video in real-time from a camera placed strategically to capture traffic scenes and thereafter counts and classify the vehicles as they cross a detection line. A visualization of the results is displayed onscreen in real-time and the count data for all vehicle classes are saved in a database for future analysis. The counting and classification obtained accuracy is greater than 80%. This research achieved a software-based video counting system that runs on computer vision algorithms and presents an accurate, inexpensive, flexible, scalable and non-intrusive approach to obtaining vehicle count on highways.

Author Biographies

  • O. T. Ajayi , Department of Surveying and Geoinformatics, College of Environmental Sciences, Bells University of Technology, Ota, Ogun State, Nigeria
    • M.Sc. Graduate of the university of Lagos.
  • J. O. Olusina, Department of Surveying and Geoinformatics, Faculty of Engineering, University of Lagos, Lagos, Nigeria

    Professor in Surveying and Geoinformatics department, Faculty of Engineering, University of Lagos, Nigeria.

References

[1] Abdullah, A. and Oothariasamy, J. “Vehicle Counting using Deep Learning Models: A Comparative Study”, (IJACSA) International Journal of Advanced Computer Science and Applications, 11(7), 2020, p. 697-700.

[2] Anastasiu, D. C., Gaul, J., Vazhaeparambil, M., Gaba, M. and Sharma, P. “E_cient City-Wide Multi-Class Multi-Movement Vehicle Counting: A Survey”, Journal of Big Data Analytics in Transportation manuscript. 2020.

[3] Awang, S. and Azmi, N. “Vehicle counting system based on vehicle type classification using deep learning method”, IT Convergence and Security 2017 Conference, 2018, pp. 52-59. Springer, Singapore.

[4] Bui, N., Yi, H. and Cho, J. “A vehicle counts by class framework using distinguished regions tracking at multiple intersections”, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2020, pp. 578-579.

[5] Dai, Z., Song, H., Wang, X., Fang, Y., Yun, X., Zhang, Z. and H. Li, “Video-based vehicle counting framework”, Institute of Electrical and Electronic Engineers (IEEE), Volume 7, 10.1109/ACCESS.2019.2914254, 2019, pp. 64460-64468.

[6] Kocatepe, A., Ulak, M. B., Kakareko, G., Pinzan, D., Cordova, J., Ozguven, E. E., Jung, S., Arghandeh, R. and Sobanjo, J. O. “Assessment of Emergency Facility Accessibility in the Presence of Hurricane-Related Roadway Closures and Prediction of Future Roadway Disruptions”, Presented at 97th Annual Meeting of the Transportation Research Board, Washington, D.C., 2018.

[7] Kulkarni, A. P., and Baligar, V. P. “Real Time Vehicle Detection, Tracking and Counting Using Raspberry-Pi”, Proceedings of the Second International Conference on Innovative Mechanisms for Industry Applications (ICIMIA 2020), 2020, pp. 603-606. IEEE.

[8] Lin, J. P. and Sun, M. T. “A YOLO-based traffic counting system”, 2018 Conference on Technologies and Applications of Artificial Intelligence (TAAI), pp. 82-85. IEEE, 2018.

[9] National Bureau of Statistics “Transport statistics”, National Bureau of Statistics (NBS), 2015, pp. 2 and 3. www.nigerianstat.gov.ng/pd fuploads/transport.pdf

[10] Olusina, J. O. “Modelling Traffic Congestion using Analytic Hierarchy Process in a Geomatics Environment: a Case Study of Lagos State”, A Ph.D. Thesis, Dept. of Surveying & Geoinformatics, University of Lagos, 2008, pp. 1 & 2.

[11] Oni, A. A. and Kajoh, N. “Video-Based Vehicle Counting System for Urban Roads in Nigeria Using Yolo and DCF-CSR Algorithms”, International Journal of Engineering Research and Technology, 12(12), 2019, pp. 2552-2556. International Research Publication House.

[12] Zheng, P., and Mike, M. “An investigation on the manual traffic count Accuracy”, 8th International Conference on Traffic and Transportation Studies (ICTTS 2012), 2012, p. 231.

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Published

2025-01-08

Issue

Section

Computer, Telecommunications, Software, Electrical & Electronics Engineering

How to Cite

VEHICLE DETECTION, TRACKING, COUNTING AND CLASSIFICATION USING DEEP LEARNING. (2025). Nigerian Journal of Technology, 43(4), 738 – 742. https://doi.org/10.4314/njt.v43i4.13