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.