一种基于核K-SVD和稀疏表示的车辆识别方法

A Vehicle Recognition Method Based on Kernel K-SVD and Sparse Representation

车辆分类与识别是智能交通的重要研究内容.针对车辆识别中的两类监督分类问题,提出一种基于核KSVD字典训练结合稀疏表示的分类方法.该方法首先利用PCA对训练图像(车辆类、非车辆类)进行特征提取及降维,然后对所得矩阵利用核函数映射到高维空间,应用K-SVD方法训练高维特征矩阵,分别得到相应的两类特征字典.最后在对测试图像进行分类时,利用基于l1最小化稀疏系数训练图像线性表示测试图像.文中给出该方法与其他几种经典方法的实验比较,重点是遮挡情况下的分类效果.实验结果表明,该方法识别率有明显改善,能够有效消除部分遮挡对车辆识别的影响.

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.