Development of a Cost‐Effective Data Collection System for Monitoring Urban Traffic Using Automated Intelligent Image Processing and Machine Vision

Abstract

This work presents a method for car license plate (LP) detection utilizing machine learning techniques that ensures rapid image processing and high detection accuracy. Three primary contributions distinguish this research. First, we introduce a novel image representation known as the “Integral Image,” which facilitates the rapid computation of features for our detector. The system is trained using a diverse set of positive (LP) and negative (non‐LP) images and is validated against various real‐world scenes. Second, we implement an AdaBoost‐based learning algorithm to identify a minimal subset of critical visual features, leading to the development of efficient classifiers. Last, our cascade method integrates increasingly complex classifiers, enabling the rapid elimination of background regions while focusing computational efforts on promising object‐like areas. The Open Computer Vision (CV) Library and Python are employed to demonstrate this implementation. We evaluated our algorithm on a dataset of 3599 images sourced from traffic video footage, achieving an impressive success rate of 95.8% in detecting Hungarian LPs.