1 research outputs found
Deep 2.5D Vehicle Classification with Sparse SfM Depth Prior for Automated Toll Systems
Automated toll systems rely on proper classification of the passing vehicles.
This is especially difficult when the images used for classification only cover
parts of the vehicle. To obtain information about the whole vehicle. we
reconstruct the vehicle as 3D object and exploit this additional information
within a Convolutional Neural Network (CNN). However, when using deep networks
for 3D object classification, large amounts of dense 3D models are required for
good accuracy, which are often neither available nor feasible to process due to
memory requirements. Therefore, in our method we reproject the 3D object onto
the image plane using the reconstructed points, lines or both. We utilize this
sparse depth prior within an auxiliary network branch that acts as a
regularizer during training. We show that this auxiliary regularizer helps to
improve accuracy compared to 2D classification on a real-world dataset.
Furthermore due to the design of the network, at test time only the 2D camera
images are required for classification which enables the usage in portable
computer vision systems.Comment: Submitted to the IEEE International Conference on Intelligent
Transportation Systems 2018 (ITSC), 6 pages, 4 figures; changed format in
compliance with adapted IEEE templat