Multi-component vehicle type recognition using adapted CNN by optimal transport

As a core element of the future smart city, intelligent transportation system provides comprehensive traffic analysis and prediction capabilities for traffic management. Vehicle type recognition, one of the indispensable parts of intelligent transportation, plays an important role in vehicle supervision, intelligent driving, public safety, and other aspects. However, fine-grained model recognition faces great challenges due to the substantial inter-category difference such as the complex background and various angles, as well as insufficient training data. This paper proposes a novel approach driven by small dataset, which finds the discriminative components based on the Faster-RCNN. Individual classifiers are trained for each component. The contribution of each component to vehicle type recognition can be evaluated through pre-trained VGG network. Considering the mutual dependency of these components, the component features are aggregated. It is assumed that the composited component features of the same model are on the potential manifold in the high-dimensional space; the SVM classifier operated at the category level is trained in the optimal transportation domain of the component features, which can enhance the discrimination ability of the feature representation. The experimental results show that the recognition accuracy of the proposed model in the Stanford BMW-10 dataset is 84.81%, which is obviously better than other models.