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Design of an Efficient Real-Time Algorithm Using Reduced Feature Dimension for Recognition of Speed Limit Signs

机译:使用减小的特征尺寸来设计高效实时算法,以识别速度限制迹象

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We propose a real-time algorithm for recognition of speed limit signs from a moving vehicle. Linear Discriminant Analysis (LDA) required for classification is performed by using Discrete Cosine Transform (DCT) coefficients. To reduce feature dimension in LDA, DCT coefficients are selected by a devised discriminant function derived from information obtained by training. Binarization and thinning are performed on a Region of Interest (ROI) obtained by preprocessing a detected ROI prior to DCT for further reduction of computation time in DCT. This process is performed on a sequence of image frames to increase the hit rate of recognition. Experimental results show that arithmetic operations are reduced by about 60%, while hit rates reach about 100% compared to previous works.
机译:我们提出了一种实时算法,用于识别来自移动车辆的速度限制迹象。通过使用离散余弦变换(DCT)系数来执行分类所需的线性判别分析(LDA)。为了减少LDA中的特征尺寸,通过从通过训练获得的信息导出的设计判别函数来选择DCT系数。在通过在DCT之前预处理检测到的ROI获得的感兴趣区域(ROI)进行二值化和变薄,从而进一步减少DCT中的计算时间。在图像帧的序列上执行该过程,以增加识别的命中率。实验结果表明,与之前的作品相比,算术运算减少了约60%,达到约100%。

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