Abstract:
This research paper explores the application of OpenCV (Open-Source Computer
Vision) technology in the field of traffic management. With the increasing number of
vehicles on roads worldwide, efficient traffic management systems are crucial to ensure
smooth and safe traffic flow. The paper begins by introducing the concept of OpenCV,
a powerful open-source library widely used for computer vision tasks. It highlights the
versatility and robustness of OpenCV in various applications, including traffic
management. The research focuses on utilizing OpenCV algorithms for real-time traffic
analysis, vehicle detection, tracking, and speed detection. To achieve accurate and
reliable results, the research incorporates advanced computer vision techniques such as
edge detection, image segmentation, feature extraction, and machine learning. A
comparative analysis has been made to find out the best results among different
techniques. Firstly, vehicle detection is done using image processing technique and
result calculated. Secondly, the system is tested with YOLO V8 which is a pretrained
machine learning model. Result shows system developed with YOLO V8 provides
much better accuracy and reliability. We got 97% accurate result while doing this
research paper using YOLO V8. Thus, whole system is developed with this approach
to detect and manage traffic system.
