Fine-grained vehicle recognition by deep Convolutional Neural Network
Kun Huang, Bailing Zhang · 2016
Vehicle recognition has been an important topic in intelligent transportation. However, to recognize different vehicle models from a same make is difficult as there are many near-identical cars under different brand names. In this paper, we investigated fine-grained vehicle recognition via deep Convolutional Neural Network (CNN). Vehicle and the corresponding parts are localized with the help of Region-based Convolutional Neural Networks (RCNN) and their features from a set of pre-trained CNNs are aggregated to train a SVM classifier. We created a fine-grained vehicle dataset and performed subsequent experiments, with preliminary results showing the potentials of the method.