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On circular traffic sign detection and recognition

机译:论圆形交通标志的检测与识别

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Automatic traffic sign detection and recognition play crucial roles in several expert systems such as driver assistance and autonomous driving systems. In this work, novel approaches for circular traffic sign detection and recognition on color images are proposed. In traffic sign detection, a new approach, which utilizes a recently developed circle detection algorithm and an RGB-based color thresholding technique, is proposed. In traffic sign recognition, an ensemble of features including histogram of oriented gradients, local binary patterns and Gabor features are employed within a support vector machine classification framework. Performances of the proposed detection and recognition approaches are evaluated on German Traffic Sign Detection and Recognition Benchmark datasets, respectively. The results of the experimental work reveal that both approaches offer comparable or even better performances with respect to the best ones reported in the literature and are compatible to real-time operation as well. (C) 2015 Elsevier Ltd. All rights reserved.
机译:自动交通标志检测和识别在诸如驾驶员辅助和自动驾驶系统之类的多个专家系统中起着至关重要的作用。在这项工作中,提出了在彩色图像上进行圆形交通标志检测和识别的新方法。在交通标志检测中,提出了一种新方法,该方法利用了最近开发的圆形检测算法和基于RGB的颜色阈值技术。在交通标志识别中,支持向量机分类框架内采用了包括定向梯度直方图,局部二进制模式和Gabor特征在内的多种特征。分别在德国交通标志检测和识别基准数据集上评估了提出的检测和识别方法的性能。实验工作的结果表明,相对于文献中报道的最佳方法,这两种方法都具有可比甚至更好的性能,并且还与实时操作兼容。 (C)2015 Elsevier Ltd.保留所有权利。

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