Vehicle type classification using unsupervised convolutional neural network

In this paper, we propose an appearance-based vehicle type classification method from vehicle frontal view images. Unlike other methods using hand-crafted visual features, our method is able to automatically learn good features for vehicle type classification by using a convolutional neural network. In order to capture rich and discriminative information of vehicles, the network is pre-trained by the sparse filtering which is an unsupervised learning method. Besides, the network is with layer-skipping to ensure that final features contain both high-level global and low-level local features. After the final features are obtained, the soft max regression is used to classify vehicle types. We build a challenging vehicle dataset called BIT-Vehicle dataset to evaluate the performance of our method. Experimental results on a public dataset and our own dataset demonstrate that our method is quite effective in classifying vehicle types.