Generation of Training Data by Degradation Models for Traffic Sign Symbol Recognition
Generation of Training Data by Degradation Models for Traffic Sign Symbol Recognition
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DOI：
10.1093/ietisy/e90-d.8.1134
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发表时间：
2007-08
期刊：
影响因子：
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通讯作者：
H. Ishida;Tomokazu Takahashi;I. Ide;Y. Mekada;H. Murase
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文献类型：
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作者：
H. Ishida;Tomokazu Takahashi;I. Ide;Y. Mekada;H. Murase
提出了一种新的交通标志符号识别训练方法。车载摄像机拍摄的符号图像存在各种形式的图像退化。为了科普退化，类似退化的图像应该被用作训练数据。我们的方法从交通标志符号的原始模板人工生成这样的训练数据。退化模型和基于GA的算法，模拟实际拍摄的图像建立。所提出的方法使我们能够获得所有类别的训练数据，而无需穷尽地收集它们。实验结果表明了该方法在交通标志符号识别中的有效性。
We present a novel training method for recognizing traffic sign symbols. The symbol images captured by a car-mounted camera suffer from various forms of image degradation. To cope with degradations, similarly degraded images should be used as training data. Our method artificially generates such training data from original templates of traffic sign symbols. Degradation models and a GA-based algorithm that simulates actual captured images are established. The proposed method enables us to obtain training data of all categories without exhaustively collecting them. Experimental results show the effectiveness of the proposed method for traffic sign symbol recognition.