Multi-View Fine-Grained Vehicle Classification with Multi-Loss Learning

A computer vision solution applied to an automatic toll collection (ATC) with a subscription/membership is proposed in this paper. In this application, a unique identifier (ID) is related to a concrete vehicle and a membership. A camera system is put in place to verify that for each transaction the vehicle and the ID correspond with the actual membership data. The visual system extracts different vehicle characteristics including license plate number, make, model, color, number of axles, etc. The system then compares the extracted characteristics with those found in the membership. We focus on solving the vehicle make classification and for that, we propose a fine-grained vehicle classification that exploits the multi-camera composition of the system by feeding a convolutional neural network (CNN) with multiple views of the vehicle. Our multi-view (MV) network extracts features from the different views of the vehicle for vehicle’s make classification. The presented evaluations show that using information from different views of a vehicle improves the make classification performance in particular in more challenging toll collection scenarios.

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