8 March 2018 A method of vehicle license plate recognition based on PCANet and compressive sensing
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Proceedings Volume 10609, MIPPR 2017: Pattern Recognition and Computer Vision; 106090Z (2018) https://doi.org/10.1117/12.2285163
Event: Tenth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2017), 2017, Xiangyang, China
Abstract
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.
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Xianyi Ye, Feng Min, "A method of vehicle license plate recognition based on PCANet and compressive sensing", Proc. SPIE 10609, MIPPR 2017: Pattern Recognition and Computer Vision, 106090Z (8 March 2018); doi: 10.1117/12.2285163; https://doi.org/10.1117/12.2285163
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