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Automated toll systems rely on proper classification of the passing vehicles.This is especially difficult when the images used for classification only coverparts of the vehicle. To obtain information about the whole vehicle. wereconstruct the vehicle as 3D object and exploit this additional informationwithin a Convolutional Neural Network (CNN). However, when using deep networksfor 3D object classification, large amounts of dense 3D models are required forgood accuracy, which are often neither available nor feasible to process due tomemory requirements. Therefore, in our method we reproject the 3D object ontothe image plane using the reconstructed points, lines or both. We utilize thissparse depth prior within an auxiliary network branch that acts as aregularizer during training. We show that this auxiliary regularizer helps toimprove accuracy compared to 2D classification on a real-world dataset.Furthermore due to the design of the network, at test time only the 2D cameraimages are required for classification which enables the usage in portablecomputer vision systems.

Deep 2.5D Vehicle Classification with Sparse SfM Depth Prior for Automated Toll Systems