Pedestrian detection is a current research hotspot, which has important applications in the fields of artificial intelligence system, vehicle assistant driving system, and intelligent monitoring. In the process of pedestrian detection based on HOG feature, the HOG feature is not obvious, the SVM classifier has high computational complexity, resulting in low recognition rate and high missed detection rate, this study proposes an improved enhanced HOG feature combined with the eXtreme Gradient Boosting (XGBoost) classifier for pedestrian detection. Firstly, the original image is preprocessed to get saliency map and HOG features. Then, the contrast of HOG features is enhanced and the pedestrian detection analysis is carried out with XGBoost classifier. Tested with the INRIA dataset, the experimental results show that the proposed algorithm has a significant improvement in recognition rate and detection speed.