Effective Two-Lane Traffic Management at the University of Ibadan, Nigeria Main Gate Using Multiple Vehicle Recognition Systems

Abstract

Traffic congestion and management have posed a major challenge to many cities in the world. Intelligent traffic management system plays an important role in monitoring and enforcing traffic laws with reduced labor. This paper uses vehicle information recognition to identify unpermitted lane shunting at the University of Ibadan main gate. The vehicle recognition system captures three main details of the vehicle; its license plate, make, and colour to ensure the system, which is named UiScope, is robust enough. Machine learning and deep learning algorithms including Convolutional Neural Network (CNN), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) algorithms are used to train classifiers for vehicle make, license plate, and colour recognition. The captured details are uploaded on a Structured Query Language (SQL) database to create a blacklist of vehicles that are shunted. The querying of the database is used to determine the shunted vehicle. The success rate for plate identification is 92%, character segmentation is 87%, character recognition is 75%, and vehicle colour recognition is 78%.