A 2D Deep Boltzmann Machine for Robust and Fast Vehicle Classification

The visual and automatic classification of vehicles plays an important role in the Transport Area. Besides of security issues, the monitoring of the type of traffic in streets and highways, as well the traffic dynamics over time, allows the optimization of use and of resources related to such public infrastructure. In this work we propose a novel method, called 2D-DBM, for robust and efficient automatic vehicle classification through color images based on a DBM (Deep Boltzmann Machine) combined with bilinear projections. While the DBM training allows a robust initialization of discriminative MLP (Multilayer Perceptron) neural network parameters, the bilinear projection technique can scale down the MLP dimensions, obtaining efficiency while preserving accuracy. The proposed method was assessed on the BIT-Vehicle database, a challenging dataset consisting of frontal images of vehicles collected in a real traffic environment, and compared with a CNN (Convolutional Neural Network) and a traditional DBM (without bilinear projection). The obtained results show that, while keeping the accuracy, the new method significantly reduced the network size and the processing time.