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首页> 外文会议>International symposium 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：基于PCANet和压缩感知的车牌识别方法。

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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：传统的车牌方法的手动特征提取对变化的多样性没有很好的鲁棒性。而且，使用主成分分析网络（PCANet）提取的高维特征导致分类效率低下。为了解决这些问题，提出了一种基于PCANet和压缩感知的车牌识别方法。首先，使用PCANet从字符图像中提取特征。然后，使用稀疏测量矩阵（它是非常稀疏的矩阵，并且与压缩感测的“严格等距特性”（RIP）条件一致）来减小提取特征的维数。最后，支持向量机（SVM）用于训练和识别尺寸已减小的特征。实验结果表明，该方法在识别和时间上均优于卷积神经网络。与没有压缩感测相比，​​该方法具有较低的特征尺寸，以提高效率。

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