Histogram of Oriented Gradients (HOG) feature extraction has a slow speed and is prone to the omission of detailed features in pedestrian detection. To tackle these problems, this study proposes a novel pedestrian detection algorithm based on Gabor feature combined with fast HOG feature. Specifically, the input image is first subjected to wavelet transform and the HOG feature of the image is quickly extracted using the idea of integral image and the principal component analysis algorithm. Then the fast HOG feature is fused with the Gabor feature obtained after Gabor wavelet transform. Finally, the hybrid features are used to train the classifier for effective pedestrian detection. The experimental results on the test set show that the detection accuracy of the hybrid feature extraction method is up to 7.37% higher than that of the single feature extraction method when the same classifier is used. Therefore, the proposed algorithm can effectively improve the accuracy of pedestrian detection.