Abstract

The existing pedestrian detection algorithms are not robust in the case of noise, obstruction and illumination change. To solve the problem, we propose a pedestrian detection algorithm combining the edge features of color image with the features of Histogram of Oriented Gradient on Depth image (referred to as the HOD features). The algorithm describes overall structural features of pedestrians by using shearlet transform to extract their edge features from color images, and obtains local edge features of corresponding depth images by generating HOD features. The overall structural features and local edge features are combined to form new feature descriptors to train a SVM (support vector machine) classifier. Due to the full integration of the two types of features, the algorithm shows significant advantages in pedestrian detection in the case of interfering factors such as noise, obstruction, illumination, and similar colors. The experimental results show that the detection accuracy rate of this algorithm is 15% higher than that of other algorithms when the false-positive rate is 0.1.

中文翻译：

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具有彩色图像EDGE特征和深度图像HOG的行人检测

摘要

现有的行人检测算法在噪声，障碍物和照明变化的情况下不够鲁棒。为了解决该问题，我们提出了一种行人检测算法，该算法将彩色图像的边缘特征与深度图像上的定向梯度直方图特征（称为HOD特征）相结合。该算法通过利用剪切波变换从彩色图像中提取行人的边缘特征来描述行人的整体结构特征，并通过生成HOD特征获得相应深度图像的局部边缘特征。整体结构特征和局部边缘特征被组合以形成新的特征描述符，以训练SVM（支持向量机）分类器。由于两种功能的完全集成，在干扰因素（例如噪声，障碍物，照明和类似颜色）的情况下，该算法在行人检测中显示出显着优势。实验结果表明，当假阳性率为0.1时，该算法的检测准确率比其他算法高15％。