Vehicle Tracking and Counting through Image Processing

Abstract

Vehicle Tracking and Counting through Image Processing is an efficient system for real-time vehicle detection, classification, and counting using the powerful combination of YOLO and OpenCV. The System uses the application of machine learning in the domain of image processing for vehicle tracking and counting. OpenCV in this system is used to process the video feed or images and provide real-time visualization of the vehicles for detection. The system uses advanced computer vision techniques such as contour detection, image segmentation, and feature extraction to accurately locate and recognize individual vehicles in a scene. The detected vehicles are classified into various categories, including cars, trucks, motorcycles, and bicycles (Two & four wheelers) using the trained YOLO model. In addition to detection and classification the count of vehicles is also noted down. The primary objective of this system is to track the vehicles, distinguish and count them. This system can be used in – Traffic Signal Management and Parking Lot Monitoring. Keywords: - YOLO, OpenCV, Python, CVAT