Advanced Vehicle Tracking System With Number Plate Detection Using Deep Learning And Computer Vision

Abstract

In recent years there has been a huge increase in the population of vehicles. Tracking a particular vehicle among the traffic is done manually and it is a very time-consuming process. Manual tracking can sometimes miss the subject due to various reasons like human error. Tracking the vehicle in real-time becomes almost impossible due to the large number of vehicles passing through a particular area. This article introduces a breakthrough solution that integrates computer vision, deep learning and object tracking to revolutionize vehicle monitoring and tracking system. The Solution leverages footages from surveillance cameras which are placed throughout the landscape. Our advanced vehicle tracking system aims to overcome the limitations of tradition manual tracking methods. It does it by analyzing the footages and gathering the data from it, then the data is processed to track the vehicle across various locations. The system can get data from already existing AI cameras directly or process the footages and use the data from it. The project aims to use YOLO V8 and Optical character recognition (OCR) System to extract data from the footages. The System will not only dynamically track vehicles, but also map vehicle trajectories on a map, providing valuable insights into traffic patterns. This article details the project’s objectives, methodology and expected outcomes of an innovative approach to transport management and tracking.