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New Vehicle Classification Method Based on Hybrid Classifiers

In this work, a real world problem of the vehicle type classification for automatic toll collection is considered. This problem is very challenging because any loss of accuracy even of the order of 1% quickly turns into a significant economic loss. To deal with such problem, many companies currently use Optical Sensors (OS) and human observers to correct the classification errors. Herein, a novel vehicle classification method is proposed. It consists in regularizing the problem using one camera to obtain vehicle class probabilities using a set of Convolutional Neural Network (CNNs) models, followed by the Gradient Boosting based classifier to fuse the continuous class probabilities with the discrete class labels obtained from optical sensors. We evaluate our method using a challenging dataset collected from the cameras of the toll collection points of the VINCI Autoroutes French network. Results show that our method performs significantly better than the existing automatic toll collection system and, hence will vastly reduce the workload of human operators.