Optimal Ensemble Classifiers Based Classification for Automatic Vehicle Type Recognition

In this work, a challenging vehicle type classification problem for automatic toll collection task is considered, which is currently accomplished with an Optical Sensors (OS) and corrected manually. Indeed, the human operators are engaged to manually correct the OS misclassified vehicles by observing the images obtained from the camera. In this paper, we propose a novel vehicle classification algorithm, which first uses the camera images to obtain the vehicle class probabilities using several Convolutional Neural Networks (CNNs) models and then uses the Gradient Boosting based classifier to fuse the continuous class probabilities with the discrete class labels obtained from two optical sensors. We train and evaluate our method using a challenging dataset collected from the cameras of the toll collection points. Results show that our method performs significantly (98.22% compared to 75.11%) better than the existing automatic toll collection system and, hence will vastly reduce the workload of the human operators. Moreover, we provide an in-depth analysis w.r.t. the learning strategies:e.g., choice of the optimization algorithm of the CNN model. Our results and analysis highlights interesting perspectives and challenges for the future work.