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Authors:
| Fahmida Islam | Department of Computer Science and Engineering, The People’s University of Bangladesh |
| Jesmin Akter | Department of Computer Science and Engineering, The People’s University of Bangladesh |
| Husne Farah | Department of Computer Science and Engineering, The People’s University of Bangladesh |
| Prome Saha Resha | Department of Computer Science and Engineering, The People’s University of Bangladesh |
https://oajea.hafizlab.com/article/01-02-006 
https://doi.org/10.64886/oajea.0102.006 
Index Terms:
IoT, Vehicle Speed Detection, Accident Detection, Computer Vision, YOLOv8, ByteTrack, OpenCV, Traffic Monitoring, Intelligent Transportation System, Deep Learning, Smart City.Abstract:
Road traffic accidents and overspeeding remain critical public safety challenges worldwide, disproportionately affecting rapidly urbanizing regions with limited automated enforcement capacity. This paper presents an IoT-Enabled Speed and Accident Detection Platform that incorporates multi-object tracking and deep-learning based object recognition, and calibration-based speed estimation into a unified, real-time intelligent traffic monitoring framework. The proposed system employs YOLOv8 for accurate, high-speed vehicle detection; ByteTrack for consistent persistent identity assignment across consecutive video frames; and a pixel-to-real-world-distance calibration method for precise vehicle speed computation. The platform automatically classifies vehicle types, identifies overspeeding behavior against configurable speed thresholds, generates real-time visual alerts, counts traffic volume through a configurable virtual line-crossing mechanism, and supports future IoT and cloud-platform integration for accident detection, remote monitoring, and emergency response automation. Experimental evaluation on traffic video sequences recorded at an urban intersection in Dhaka, Bangladesh, shows a mean Average Precision (mAP@0.5) as 0.82, a Multi-Object Tracking Accuracy or MOTA of 0.75, an IDF1 of 0.79, and a speed estimation Mean Absolute Error or MAE as 3.2 km/h. The reconfigurable, scalable architecture positions this platform as a practical and cost-effective foundation for intelligent transportation systems (ITS) and smart city infrastructure.Conclusion:
This paper presented an IoT-Enabled Speed and Accident Detection Platform integrating YOLOv8-based vehicle detection, ByteTrack multi-object tracking, and calibration-based speed estimation into a modular, real-time, and IoT-extensible intelligent traffic monitoring system. The platform addresses the critical shortcomings of traditional manual and semi-automated traffic enforcement by delivering automated, continuous, and scalable video analytics without requiring specialized sensor hardware. Experimental evaluation on real-world urban traffic sequences confirmed high detection accuracy (mAP@0.5 = 0.82), robust tracking performance (IDF1 = 0.79, MOTA = 0.75), and practical speed estimation accuracy (MAE = 3.2 km/h). The comparative analysis demonstrates that the proposed system achieves superior or competitive performance relative to established baselines across all evaluated dimensions while additionally providing IoT integration capability.The modular architecture—with clear separation between the input, processing, analytics, and output layers—facilitates independent upgrading of individual components as more powerful detection models, tracking algorithms, or IoT protocols become available. This design positions the platform as a durable and adaptable foundation for intelligent transportation systems and smart city infrastructure.
License:
Articles published in OAJEA are licensed under a Creative Commons Attribution 4.0 International License.Cite This Paper:
F. Islam, J. Akter, H. Farah, P.S. Resha “IoT-Enabled Speed and Accident Detection Platform Using Deep Learning and Multi-Object Tracking”, Open Access Journal on Engineering Applications (OAJEA), Volume No. 01, Issue No. 02, Page 52-59, May, 2026. https://doi.org/10.64886/oajea.0102.006Reference:
World Health Organization, "Global Status Report on Road Safety 2023," WHO Press, Geneva, Switzerland, 2023.
Bangladesh Road Transport Authority (BRTA), "Road Accident Statistics Report 2022," BRTA, Dhaka, Bangladesh, 2022.
S. A. Alam and M. R. Islam, "Intelligent Traffic Management System: A Review of Challenges and Opportunities in Developing Countries," Journal of Transportation Engineering, vol. 148, no. 4, pp. 1–14, 2022.
M. Zanella, N. Bui, A. Castellani, L. Vangelista, and M. Zorzi, "Internet of Things for Smart Cities," IEEE Internet of Things Journal, vol. 1, no. 1, pp. 22–32, 2014.
Y. Li, H. Zhang, and Z. Wang, "Traffic Flow Detection and Speed Estimation Based on Video Processing," International Journal of Computer Applications, vol. 175, no. 23, pp. 1–6, 2020.
M. Bramberger, A. Doblander, A. Maier, B. Rinner, and H. Schwabach, "Distributed Embedded Smart Cameras for Surveillance Applications," Computer, vol. 39, no. 2, pp. 68–75, 2006.
A. Bochkovskiy, C.-Y. Wang, and H.-Y. M. Liao, "YOLOv4: Optimal Speed and Accuracy of Object Detection," arXiv preprint arXiv:2004.10934, 2020.
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, "You Only Look Once: Unified, Real-Time Object Detection," in Proc. IEEE/CVF CVPR, Las Vegas, NV, USA, 2016, pp. 779–788.
J. Redmon and A. Farhadi, "YOLOv3: An Incremental Improvement," arXiv preprint arXiv:1804.02767, 2018.
A. Bewley, Z. Ge, L. Ott, F. Ramos, and B. Upcroft, "Simple Online and Realtime Tracking," in Proc. IEEE ICIP, Phoenix, AZ, USA, 2016, pp. 3464–3468.
N. Wojke, A. Bewley, and D. Paulus, "Simple Online and Realtime Tracking with a Deep Association Metric," in Proc. IEEE ICIP, Beijing, China, 2017, pp. 3645–3649.
Y. Zhang, P. Sun, Y. Jiang, D. Yu, F. Weng, Z. Yuan, P. Luo, W. Liu, and X. Wang, "ByteTrack: Multi-Object Tracking by Associating Every Detection Box," in Proc. ECCV, Tel Aviv, Israel, 2022, doi: 10.1007/978-3-031-19766-0_21.
L. Chen, C. Englund, and L. Jonsson, "Cooperative Intelligent Transport Systems: A Review," IEEE Transactions on Intelligent Transportation Systems, vol. 19, no. 12, pp. 3888–3901, 2018.
N. Mahamkali and V. Ayyasamy, "OpenCV for Computer Vision Applications," in Proc. Nat. Conf. on Big Data and Cloud Computing (NCBDC'15), Trichy, India, Mar. 2015.
R. E. Kalman, "A New Approach to Linear Filtering and Prediction Problems," ASME Journal of Basic Engineering, vol. 82, no. 1, pp. 35–45, 1960.
M. H. Rahman, M. Naderuzzaman, "A Comprehensive Review of M2M Communication Protocols," Open Access Journal on Engineering Applications (OAJEA), Volume No. 01, Issue No. 01, Page 1-13, July, 2025. https://doi.org/10.64886/oajea.0101.001
Journal Information
| Frequency: Quarterly |
| Submission to First Decision: 7 days |
| Submission to Acceptance: 30 days |
| Accept to Publish: 15 days |
| Article Processing Charges (APCs): None |
