基于多通道融合HOG特征的全天候运动车辆检测方法

A Method of Moving Vehicle Detection in All-weather Basedon Melted Multi-channel HOG Feature

  • 摘要: 对传统的梯度方向直方图(HOG)特征提取方法进行改进,提出了一种基于多通道特征提取的加权HOG特征融合方法。首先采用基于亮度均值的方式对彩色车辆图像增强处理,其次采用自适应加权法将H、S、V三通道的梯度方向直方图(HOG)特征融合成多通道融合HOG特征,最后采用支持向量机(SVM)对融合后的特征进行车辆分类器训练和车辆检测。该方法与直接运用HOG特征进行车辆检测以及其他车辆检测方法相比,具有检测率高、鲁棒性强等特点,并且在各种气候环境下都能实现较好的检测效果,效果优于其他方法,达到了全天候车辆检测的要求。

     

    Abstract: In this article,an algorithm for vehicle detection is proposed using the improved histogramof oriented gradients(HOG)features,in which a fusion of weighted multi-channel HOG features isemployed.First,the color image is enhanced by the method based on the mean of brightness.Afterthat the image is remapped into HSV color space.In consequence,the multi-channel HOG feature isobtained by combining the adaptive-weight HOG feature for each channel.Finally,the support vectormachine(SVM)is trained by such feature to detect the vehicles.Experiments prove the robustnessand precision of this algorithm and it reaches the requirement for all-weather vehicles detecting interms of the one using the traditional HOG features.

     

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