Enhancing Road Safety and Traffic Management: Real-Time Vehicle Speed Detection Using OpenCV

Abstract

Reducing traffic jams, increasing road safety, and enhancing the functionality of automated traffic systems all depend on accurate vehicle speed measurements. In this work, we demonstrate a real-time approach to vehicle speed detection using the well-known computer vision library OpenCV. Using methods like background subtraction, contour detection, and motion tracking, the system analyzes video frames to identify moving vehicles and track their movements. The system can provide accurate and fast speed readings by measuring how far a vehicle moves between frames and translating that pixel movement into actual speed. Tests conducted in various traffic and lighting conditions demonstrate that this approach is reliable and economical. Overall, the findings indicate thatOpenCV-based approaches can support smart transportation systems and can be further improved using advanced tracking methods and machine learning.

Introduction

The text discusses the development and evaluation of a computer-vision-based vehicle speed detection system using OpenCV as a cost-effective alternative to traditional traffic monitoring methods such as radar and induction loops. With rising vehicle numbers and speeding being a major cause of accidents and congestion, accurate and affordable speed measurement systems are increasingly important for traffic management.

The proposed approach estimates vehicle speed by analyzing motion across consecutive video frames using techniques such as background subtraction, contour detection, object tracking, and centroid tracking. These methods allow vehicle detection and tracking using existing CCTV infrastructure, reducing installation and maintenance costs. However, challenges such as overlapping vehicles, shadows, and varying lighting conditions are acknowledged.

The study follows a Systematic Literature Review (SLR) methodology, examining research published between 2015 and 2025 from reputable sources. The reviewed methods were analyzed through a structured OpenCV-based workflow involving video preprocessing, vehicle detection and tracking, speed estimation using calibration factors, and validation against manually recorded data. The system was implemented using Python, OpenCV, NumPy, Pandas, and Matplotlib, enabling real-time processing.

Results indicate that the OpenCV-based system achieves high accuracy with low error rates, operates in real time, and remains robust under different traffic and environmental conditions. Although performance slightly degrades in heavy traffic with overlapping vehicles, overall findings confirm that the approach is reliable, affordable, and practical for real-world traffic monitoring and speed enforcement applications.

Conclusion

This study demonstrated that computer vision can provide a precise, affordable, and scalable solution for traffic monitoring by introducing a real-time vehicle speed detection system developed with OpenCV. The technology generated accurate speed readings and was able to identify and follow cars in various lighting and traffic situations. It performs well for practical applications including intelligent transportation systems and road safety monitoring, according to tests on accuracy, processing speed, and system stability. The overall performance indicates that OpenCV-basd techniques can be a potent substitute for conventional speed detection tools, even though the system encountered some difficulties in scenarios with high traffic or overlapping vehicles. In order to increase accuracy and improve the system\'s adaptability in complex traffic settings, future improvements might make use of sophisticated machine learning models.

References

[1] A.?Siddiqua, A. Saher, and Sumera, “Vehicle Speed Detection using Haar Cascade Classifier and Correlation Tracking,” Indian Journal of Science and Technology, vol. 17, no. 8, pp. 741–750, Feb. 2024 [2] Y.?S. Lakshmi, P. N. Sri, S. Mounika, T. Usha Sri, and Y. Suresh, “Vehicle Speed Estimation Using OpenCV Framework,” International Journal of Computer Sciences and Engineering, vol. 11, no. 11, pp. 17–21, Nov. 2023 [3] A.?Hiwarekar, S. Chavhan, O. Deshpande, and V. Joshi, “Vehicle Speed Estimation using Object Detection for Intelligent Traffic Management,” in Artificial Intelligence and Communication Technologies, SCRS, India, 2023. [4] D.?Jeni Priya, G. Raghavi and Dr. Roselin Mary, “Vehicle Speed Estimation and Detection Using OpenCV,” International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), 2022 [5] M.?Ibrahim, B.?S. Divya, and S. Thompson, “Computer Vision based Vehicle Detection and Speed Estimation for Road Safety,” Easy Chair Preprint 7099, 2021

Copyright

Copyright © 2025 Husen Momin, Danish Momin, Mohammad Altmas, Dr. Veena Kadam . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.