Advanced Over Speeding Detection System based on Time, Distance & Tyre Circumference

Advanced Over Speeding Detection System based on Time, Distance & Tyre Circumference

Devanshu Vijay Patil, Nehal Mahendra Rane

Devanshu Vijay Patil, Computer Engineering, Zeal College of Engineering and Research

Nehal Mahendra Rane, Computer Engineering, Zeal College of Engineering and Research

Abstract - The review of methodologies regarding real-time vehicle speed detection, estimation, and tracking using some advanced computer vision techniques, using modern deep learning techniques, by applying models like YOLOv8, Deep SORT, and GMM by possible application for the accurate detection and tracking of vehicles. Thus, the research refers to fewer techniques that have achieved a Mean Absolute Error of 35 and RMSE of 422 relating to the estimation of speed; it refers to the addition of better time interpolation methods, including vehicle acceleration that lowers the error in the speed estimation to 07% levels. Another discussion incorporated in the paper is the high-resolution dataset for fine-grained vehicle recognition and Classification, Low-Cost Speed Detection Systems that rely on frame difference methods and IoT integration These innovations enhance the real-time surveillance ability especially in sensitive areas such as schools and hospitals, as it is efficient, although some challenges brought about by environmental factors such as lighting conditions, angles, and other cameras bring about difficulties. Below are the results from the survey and indicate in what ways this could be attributed to developing better traffic management, police service, and intelligent transportation systems. Results Specifically, these reveal the feasibility of scalable, accurate, and cost-effective solutions towards road safety and effective traffic management.

Key Words:  YOLOv8, Deep SORT, Speed estimation error, Vehicle recognition, IoT integration, Traffic management