利用HOG-LBP自适应融合特征实现禁令交通标志检测

针对我国圆形禁令交通标志，提出一种快速、稳定的标志自动检测方法。首先，基于RGB空间对标志图像进行颜色分割，针对三通道差值的固定阈值分割方法对图像亮度的适应性较差的问题，提出基于基础通道选择的相对通道差值计算及阈值曲线拟合的方法进行自适应颜色分割，并针对标志与背景同色的情况，提出梯度过滤的方法将标志与同色背景分离；然后对分割图像进行边缘提取，基于标志椭圆特征，用最小二乘椭圆拟合后验偏差估计法来对边缘进行筛选，并提取最终标志边缘。实验表明，该方法能快速准确地对自然环境下不同亮度的圆形禁令标志自动检测，具有实时性和鲁棒性，在智能交通中具有良好的应用前景。

HOG-LBP Adaptable Fused Features Based Medthod for Forbidden Traffic Signs Detection

In this paper, a new fast, stable automatic detection method for round ban traffic signs is proposed. Color segmentation based on RGB color space is conducted on sign images. Given the poor adaptability of segmenting by calculating direct difference between channels and setting fixed thresholds, a novel self-adaptive method is proposed that calculates the relative channel difference and fitting threshold curve based on selecting a basic channel. Meanwhile, a gradient filter method separates the signs from its background with the same red color. Edges are extracted and a method of error estimation after least square ellipse fitting screens out the sign edges. Experimental results show that the method presented in this paper can automatically detect round ban traffic signs from sign images of deferent brightness in natural environments, with good prospects in Intelligent transportation as it is stable and applicable in real-time.