行人检测是目标识别领域的一大难点。现阶段用于行人检测的特征维数都比较高，为克服高维特征对实时性的影响，本文运用主元分析(PCA)对特征进行降维，加快检测速度。单一特征的信息有限，本文运用基于线性鉴别分析 (LDA)的线性权重融合原则对一些底层特征 (颜色、梯度、直方图 )和多层次导向边缘能量特征进行特征融合使特征具有多源信息。且上述特征可采用积分图技术进行快速计算，所以行人检测系统的鲁棒性和实时性得到加强。在目标识别领域直方图交叉核支持向量机(HIKSVM)具有分类快，且准确率高的优点，采用其进行分类，系统实时性更进一步提升。实验表明本文方法检测速度和检测率优于经典的 HOG+SVM算法。

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.