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首页> 外文期刊>Neural computing & applications >Traffic sign recognition based on color, shape, and pictogram classification using support vector machines
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Traffic sign recognition based on color, shape, and pictogram classification using support vector machines

机译：基于颜色，形状和象形图的交通标志识别使用支持向量机

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摘要

Traffic sign recognition is the second part of traffic sign detection and recognition systems. It plays a crucial role in driver assistance systems and provides drivers with crucial safety and precaution information. In this study, the recognition of the TS is performed based on its border color, shape, and pictogram information. This technique breaks down the recognition system into small parts, which makes it efficient and accurate. Moreover, this makes it easy to understand TS components. The proposed technique is composed of three independent stages. The first stage involves extracting the border colors using an adaptive image segmentation technique that is based on learning vector quantization. Then, the shape of the TS is detected using a fast and simple matching technique based on the logical exclusive OR operator. Finally, the pictogram is extracted and classified using a support vector machines classifier model. The proposed technique is applied on the German traffic sign recognition benchmark and achieves an overall recognition rate of 98.23%, with an average computational speed of 30ms.
机译：交通标志识别是交通标志检测和识别系统的第二部分。它在驾驶员辅助系统中发挥着至关重要的作用，并提供了具有重要安全和预防信息的驱动因素。在该研究中，基于其边界颜色，形状和象形图进行了对TS的识别。该技术将识别系统分成小部分，这使得其高效准确。此外，这使得易于理解TS组件。所提出的技术由三个独立阶段组成。第一阶段涉及使用基于学习矢量量化的自适应图像分割技术提取边框颜色。然后，使用基于逻辑排他性或操作员的快速和简单的匹配技术来检测TS的形状。最后，使用支持向量机分类器模型提取和分类象形图。所提出的技术适用于德国交通标志识别基准，实现了98.23％的整体识别率，平均计算速度为30ms。

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