Pedestrian detection is a major difficulty in object recognition. Features used for pedestrian detection are high in dimension. We use principal component analysis to reduce the dimension of features, and make the detection algorithm run faster. It overcomes the influence of the high dimensional features which reduce the real-time of pedestrian detection. The information content of single feature is limited. To make use of multi-source information feature, we fusion some low-level features (color、gradient、histogram) and multi-level oriented edge energy feature based on the linear discriminant analysis of linear weighted fusion strategy. Features can be calculated fast by integral image technique. The robustness and real-time performance of pedestrian detection system have been strengthened. Histogram intersection kernel support vector machine have the advantage of fast classification and high accuracy in object recognition. It can be used for further enhancing the system real-time performance. The experiments show that the proposed algorithm has faster detection speed and higher precision than the classical algorithm HOG+SVM.