Abstract：The classification and recognition of vehicle is of great importance in the research of intelligent transportation system. A method based on PCA, kernel K-SVD and sparse representation classification method is proposed for two-class supervised classification. Firstly, PCA is used in this method to train both vehicle and non-vehicle images for feature extraction and dimensionality reduction. Then, the Gaussian-Kernel function is used to map the matrix to the high-dimensional space, and K-SVD is applied to train the high-dimension feature matrix for two corresponding dictionaries in this space. Finally, training images based on l1-minimization sparse coefficient are used to linearly represent test images. The experiments are carried out and the results show that the performance of the proposed method on the partially-covered case is obviously better than that of other classical methods.
孙锐，王晶晶. 一种基于核K-SVD和稀疏表示的车辆识别方法*[J]. 模式识别与人工智能, 2014, 27(5): 435-442.
SUN Rui,WANG Jing-Jing. A Vehicle Recognition Method Based on Kernel K-SVD and Sparse Representation. , 2014, 27(5): 435-442.
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2014, Vol. 27 
