Image Processing and Distributed Computing for License Plate Tracking System

Abstract

The study of the proposed license plate tracking system implemented by image processing, distributed computing, and machine learning techniques prioritizes improving the accuracy and effectiveness of license plate recognition in real-world applications. It applies image harvesting via cameras and implements an image enhancement process to improve the quality. The license plate detections are realized using advanced object detection techniques, and the characters on the plates have good OCR performance. It is a parallel system for distributed computing, which assigns specific processing tasks to different entities involved in the process to accomplish operations faster and expand the system. Iterative machine learning models, trained on many tagged datasets, are implemented to improve inference and tracking. Database integration will allow us to update registered license plates frequently to log the information about detected license plates in real time. Security measures, e.g., data encryption and control of authorizations, protect the data against disclosure to unauthorized persons—recurring updates with feedback loops and model retraining to yield flexibility to changing environments and continuous accuracy. The proposed system presents a comprehensive approach to license plate tracking, addressing accuracy, scalability, and security challenges by integrating cutting-edge technologies