A Vehicle Classification System for Intelligent Transport System using Machine Learning in Constrained Environment

Vehicle type classification has an extensive variety of applications which include intelligent parking systems, traffic flow-statistics, toll collecting system, vehicle access control, congestion management, security system and many more. These applications are designed for reliable and secure transportation. Vehicle classification is one of the major challenges of these applications particularly in a constrained environment. The constrained environment in the real world put a limit on data quality due to noise, poor lightning condition, low resolution images and bad weather conditions. In this research, we build a more practical and more robust vehicle type classification system for real world constrained environment with promising results and got a validation accuracy of 90.85 and a testing accuracy of 87%. To this end, we design a framework of vehicle type classification from vehicle images by using machine learning. We investigate the deep learning method Convolutional neural network (CNN), a specific type of neural networks. CNN is biologically inspired with multi-layer feed forward neural networks. It can learn automatically at several stages of invariant features for the particular chore. For evaluation, we also compared the performance of our model with the performance of other machine learning algorithms like Naive Bayes, SVM and Decision Trees.