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首页> 外文会议>SPIE Conference on Multispectral Image Processing and Pattern Recognition >A method of vehicle license plate recognition based on PCANet and compressive sensing
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A method of vehicle license plate recognition based on PCANet and compressive sensing

机译：一种基于PACANET和压缩感测的车辆牌照识别方法

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摘要

The manual feature extraction of the traditional method for vehicle license plates has no good robustness to change in diversity. And the high feature dimension that is extracted with Principal Component Analysis Network (PCANet) leads to low classification efficiency. For solving these problems, a method of vehicle license plate recognition based on PCANet and compressive sensing is proposed. First, PCANet is used to extract the feature from the images of characters. And then, the sparse measurement matrix which is a very sparse matrix and consistent with Restricted Isometry Property (RIP) condition of the compressed sensing is used to reduce the dimensions of extracted features. Finally, the Support Vector Machine (SVM) is used to train and recognize the features whose dimension has been reduced. Experimental results demonstrate that the proposed method has better performance than Convolutional Neural Network (CNN) in the recognition and time. Compared with no compression sensing, the proposed method has lower feature dimension for the increase of efficiency.
机译：车辆牌照传统方法的手动特征提取对多样性的变化没有良好的稳健性。和用主成分分析网络（PCANet）提取的高特征尺寸导致降低分类效率。为了解决这些问题，提出了一种基于PCANet和压缩感测的车辆牌照识别方法。首先，PCanet用于从字符图像中提取该功能。然后，使用作为非常稀疏矩阵的稀疏测量矩阵和压缩感测的受限制的等距特性（RIP）条件来减少提取特征的尺寸。最后，支持向量机（SVM）用于训练和识别其维度降低的特征。实验结果表明，所提出的方法在识别和时间内具有比卷积神经网络（CNN）更好的性能。与无压缩感测相比，​​所提出的方法具有较低的特征尺寸，以提高效率。

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