关键词: 车型识别, 稀疏栈式编码, 卷积, 池化, 特征字典

Abstract: In order to improve the accuracy of sparse stack coding for vehicle type identification, this paper proposes a vehicle identification method based on improved sparse stack coding. The layer-by-layer unsupervised method is used to train the network structure, and the feature dictionary is learned from a large number of unmarked data sets. Then, the convolution and pooling modules are introduced on the basis of sparse stack coding, and the learned feature dictionary is taken as a convolution kernel. Feature map of the image is obtained by convolving and pooling the image containing the vehicle. Finally, supervised fine-tuning is performed on a small number of tag data sets by using the softmax classifier. By experimenting on the BIT-Vehicle dataset, the improved algorithm is superior to the traditional sparse stacking algorithm. In the dataset with less labeling, the recognition accuracy is better than the neural network algorithm.

Key words: vehicle type classification, sparse stack coding, convolution, pooling, feature dictionary