传统的基于HOG 与LBP 的特征融合行人检测方法光谱信息损失多、对噪声较为敏感，原始的LBP 算法对不均匀的光照变化鲁棒性差，对纹理特征的旋转不变性差。为了克服以上缺点，本文提出了一种基于CLBC 和HOG 特征融合的行人检测算法。首先，计算原始图像的CLBC 特征，并计算基于CLBC 纹理特征谱的HOG 特征。接着计算原始图像的HOG 特征以提取图像的边缘特征。然后将图像的三种特征融合来描述图像，并使用PCA 方法降低特征维度，最后使用HIKSVM 分类器实现最终对行人的检测。本文分别在Caltech 行人数据库和INRIA 行人数据库进行实验以验证所提出算法的有效性。实验结果表明，本文所提出的算法有效地提高了行人检测的精度。

The traditional feature fusion method based on HOG and LBP loses much spectral information, and it is more sensitive to noise. The original LBP algorithm has poor robustness to uneven illumination changes and poor rotation invariance to texture features. In order to overcome these shortcomings of the method, this paper proposes a pedestrian detection algorithm based on the feature fusion of CLBC and HOG. First, the CLBC feature of the original image is calculated, and the HOG feature based on the CLBC texture feature spectrum is calculated. The HOG feature of the original image is then calculated to extract the edge feature of the image. Then three features of the image are fused to describe the image, and after that we use principal component analysis to reduce the feature dimension. Finally, the detection of the pedestrian is realized by using the HIKSVM classifier. In this paper, experiments are carried out in Caltech pedestrian database and INRIA pedestrian database to verify the effectiveness of the proposed algorithm. The final experimental results show that the proposed algorithm improves the accuracy of pedestrian detection.