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ISSN: 2321-9653
Estd : 2013

Ijraset Journal For Research in Applied Science and Engineering Technology

Real-Time Vehicle Detection and Lane Tracking System using Advanced Computer Vision Techniques

Authors: V. Trinadha, Bh. Lasyapriya, G. Sravani, S. AvinashBhadrachari, P. Sravani, R. Sudheer

DOI Link: https://doi.org/10.22214/ijraset.2026.78783

Certificate: View Certificate

Abstract

This project introduces a Real-Time Vehicle Detection and Lane Tracking System aimed at improving traffic monitoring and road safety through advanced computer vision and deep learning. Using the YOLOv8 model, the system detects multiple vehicle classes in live video streams and assigns unique IDs through multi-object tracking. Lane boundaries are extracted using edge detection and region masking, enabling accurate lane classification for each vehicle. The system also identifies lane-crossing events and automatically logs them into a structured CSV file for further analysis. Additionally, the system estimates vehicle speed, tracks movement patterns, and generates activity logs using pixel displacement and frame-based analysis. A real-time Streamlit dashboard visualizes detections, lane overlays, and tracking information to provide a clear overview of road activity. With its modular and efficient design, the solution supports applications in smart traffic management, violation detection, and intelligent transportation systems, offering a scalable and accurate approach for real-time roadway surveillance.

Introduction

The project “Real-Time Vehicle Detection and Lane Tracking System using Advanced Computer Vision Techniques” aims to improve traffic management and road safety through an automated system that leverages YOLOv8 for vehicle detection and OpenCV for tracking and lane analysis. The system identifies multiple vehicle types, assigns unique tracking IDs, monitors lane usage and changes, estimates speed, and classifies traffic density in real time. A Streamlit interface allows interactive video analysis, and CSV logs provide structured traffic data. Compared to traditional manual or sensor-based methods, this approach is cost-effective, scalable, and capable of real-time monitoring. Limitations include reduced performance in poor weather or low-quality video, dependency on proper camera setup, and high computational requirements. Future enhancements involve IoT integration, advanced lane segmentation, accident detection, edge deployment, and city-wide traffic analytics, enabling a foundation for smart transportation systems.

Conclusion

The Real-Time Vehicle Detection and Lane Tracking System using Advanced Computer Vision Techniques was successfully developed to analyze traffic videos in an efficient and practical manner. The system is capable of detecting vehicles, tracking their movement, assigning them to different lanes, identifying lane changes, estimating speed, and calculating traffic density. By using YOLOv8, OpenCV, Python, and Streamlit, the project provides an effective solution for intelligent traffic monitoring and analysis. The developed system improves the accuracy and speed of traffic observation when compared to manual monitoring methods. It reduces human effort, provides continuous analysis, and generates useful output such as annotated video frames and CSV logs for further study. The project also demonstrates that advanced computer vision techniques can be applied successfully in real-time traffic applications with good reliability and performance.

References

[1] Redmon, J., Divvala, S., Girshick, R., and Farhadi, A., “You Only Look Once: Unified, Real-Time Object Detection,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. [2] Jocher, G., Chaurasia, A., Qiu, J., and Ultralytics Team, “Ultralytics YOLOv8 Documentation,” Ultralytics, 2023. [3] Bradski, G., “The OpenCV Library,” Dr. Dobb’s Journal of Software Tools, 2000. [4] Bochkovskiy, A., Wang, C. Y., and Liao, H. Y. M., “YOLOv4: Optimal Speed and Accuracy of Object Detection,” arXiv preprint arXiv:2004.10934, 2020. [5] Zhang, Z., “A Flexible New Technique for Camera Calibration,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000.

IJRASET78783

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Paper Id : IJRASET78783

Publish Date : 2026-03-25

ISSN : 2321-9653

Publisher Name : IJRASET

DOI Link : Click Here

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