Hybrid Motion Metric Framework for Video-Based Vehicle Speed Estimation

  This study presents a comprehensive vision-based vehicle speed estimation system that leverages traffic surveillance video data to accurately determine vehicle speed without relying on additional hardware sensors. The proposed approach addresses key limitations of traditional speed measurement techniques, such as high installation cost, maintenance complexity, and limited scalability, by utilizing existing camera infrastructure.      A major contribution of this work lies in the integration of deep learning-based detection (YOLO), robust multi-object tracking (DeepSORT), and feature-level motion tracking using optical flow techniques. Unlike conventional approaches that depend solely on bounding box tracking, the proposed method focuses on feature points near the wheel region, resulting in improved tracking stability and reduced perspective-related errors. Another significant contribution is the introduction of dual motion metrics, namely: •              Crossing distance (pixel displacement) •              Frame counter number (temporal measurement)        These metrics form the basis of two complementary speed estimation models: a distance-based model and a time-based model. Experimental results demonstrate that while the distance-based model performs reasonably well under certain conditions, the time-based model provides superior accuracy and consistency, particularly at higher frame rates.      Further enhancement using a neural network model shows that combining motion metrics with machine learning techniques can capture non-linear relationships and significantly improve prediction accuracy. The system achieves a Mean Absolute Error (MAE) of approximately 3 km/h, which is comparable to or better than many existing vision-based approaches.      The analysis also highlights the influence of factors such as frame rate, perspective distortion, and tracking accuracy on overall system performance. By incorporating feature-based tracking and structured motion measurement using virtual intrusion lines, the proposed method effectively mitigates several challenges associated with vision-based speed estimation.      Overall, the proposed system provides a cost-effective, scalable, and accurate solution for vehicle speed estimation, making it highly suitable for applications in smart traffic management, speed enforcement systems, and intelligent transportation infrastructure.   Future Work      Despite the promising results, there are several directions for future improvement: •              Development of automatic feature selection mechanisms to eliminate manual initialization •              Extension to multi-lane and high-density traffic scenarios •              Implementation of real-time processing using GPU acceleration •              Enhancement of robustness under adverse weather and lighting conditions •              Integration with smart city platforms and IoT-based traffic systems   Final Remark       In conclusion, this research demonstrates that vision-based speed estimation can serve as a practical alternative to traditional sensor-based systems, offering a balance between accuracy, efficiency, and real-world applicability. The combination of deep learning and motion-based metrics establishes a strong foundation for future advancements in intelligent transportation systems.  

1.       K. Sangsuwan and M. Ekpanyapong, “Video-Based Vehicle Speed Estimation Using Speed Measurement Metrics,” IEEE Access, vol. 12, pp. 4845–4855, 2024.   2.       J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You Only Look Once: Unified, Real-Time Object Detection,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 2016, pp. 779–788.   3.       Bewley, Z. Ge, L. Ott, F. Ramos, and B. Upcroft, “Simple Online and Realtime Tracking,” in Proc. IEEE Int. Conf. Image Process. (ICIP), 2016, pp. 3464–3468.   4.       N. Wojke, A. Bewley, and D. Paulus, “Simple Online and Realtime Tracking with a Deep Association Metric,” in Proc. IEEE Int. Conf. Image Process. (ICIP), 2017, pp. 3645–3649.   5.       B. D. Lucas and T. Kanade, “An Iterative Image Registration Technique with an Application to Stereo Vision,” in Proc. Int. Joint Conf. Artificial Intelligence (IJCAI), 1981, pp. 674–679.   6.        J. Shi and C. Tomasi, “Good Features to Track,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 1994, pp. 593–600.   7.       H. Bay, T. Tuytelaars, and L. Van Gool, “SURF: Speeded Up Robust Features,” in Proc. Eur. Conf. Comput. Vis. (ECCV), 2006, pp. 404–417.   8.       D. G. Lowe, “Distinctive Image Features from Scale-Invariant Keypoints,” Int. J. Comput. Vis., vol. 60, no. 2, pp. 91–110, 2004.   9.       R. Girshick, “Fast R-CNN,” in Proc. IEEE Int. Conf. Comput. Vis. (ICCV), 2015, pp. 1440–1448.   10.    T.-Y. Lin et al., “Focal Loss for Dense Object Detection,” in Proc. IEEE Int. Conf. Comput. Vis. (ICCV), 2017, pp. 2980–2988.