Image Processing Technique for Tracking and Counting of Vehicles Using ROI

This research paper presents a novel approach for vehicle tracking and counting utilizing the advanced object detection model YOLOv8 in the field of image processing. The accurate monitoring of vehicular traffic is crucial for various applications, including traffic management, urban planning, and safety analysis. Existing methods often struggle with real-time tracking and counting of vehicles due to challenges such as occlusions, varying lighting conditions, and complex traffic scenarios. In our proposed methodology, we leverage the power of YOLOv8, a deep learning-based object detection model, uses real-time detection to identify and monitor vehicles, ensuring high accuracy and robustness in various traffic environments. The proposed approach provides a valuable tool for traffic management authorities and urban planners, offering precise data for traffic flow analysis and infrastructure development.