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Traffic sign recognition without color information

机译:没有颜色信息的交通标志识别

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摘要

Color represents an important attribute in the field of traffic sign recognition. However, when the color of the traffic sign fades or the traffic scene is collected in gray as in the case of Infrared imaging, then color based recognition systems fail. Other problems related to color are simply that different countries use different colors. Even within the European Union, colors of traffic signs are not the same. This paper aims to present a new approach to detect traffic signs without color attributes. It is based a two-stage sliding window which detects traffic signs in the multi-scale image. Histogram of Oriented Gradients HOG descriptors are computed as a quality function which are evaluated by two SVM classifier; the coarse and the fine detectors. Different objects detected by the coarse detectors are clustered and a fine search is conducted in the areas where traffic signs are more probable to exist. Experiments conducted to detect traffic signs under different light conditions such as sunny, cloudy, fog and snow fall have showed a performance of 98% and very low false positive rate. The proposed approach was tested on the Yield traffic signs because it has a simple triangular shape which can be found in many places other than the traffic signs which represent a challenge to the proposed approach.
机译:颜色是交通标志识别领域的重要属性。但是,当交通标志的颜色变淡或交通场景被收集为灰色(如红外成像的情况)时,基于颜色的识别系统就会失效。与颜色有关的其他问题仅仅是,不同的国家使用不同的颜色。即使在欧盟内部,交通标志的颜色也不相同。本文旨在提出一种新的方法来检测没有颜色属性的交通标志。它基于两级滑动窗口,可检测多比例图像中的交通标志。定向梯度直方图HOG描述符是作为质量函数计算的,由两个SVM分类器进行评估。粗探测器和细探测器。将由粗略检测器检测到的不同对象聚集在一起,并在更可能存在交通标志的区域中进行精细搜索。为在不同的光照条件下(例如晴天,多云,雾天和大雪天)检测交通标志而进行的实验显示出98%的性能和极低的误报率。该提议的方法已在收益率交通标志上进行了测试,因为它具有简单的三角形形状,在交通标志以外的其他地方也可以找到,这对提议的方法构成了挑战。

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