关键词: 二维离散小波变换, 行人检测, 局部二值模式特征, 局部梯度模式特征, 特征融合, 支持向量机

Abstract: This paper presents a two-dimensional discrete wavelet transform Haar Local Binary Pattern (LBP) with Local Gradient Pattern ( LGP ) feature fusion method Haar-LL. The image of the two-dimensional discrete wavelet transform Haar,to thereby obtains four different frequency sub-images,and extracts the low frequency part of the LBP feature,three high-frequency sub-images of the LGP feature extraction,and takes the three characteristics of LGP parallel fusion and LBP features for serial fusion. Under the Matlab environment using Support Vector Machine(SVM) on the INRIA data set for five experimental groups INRIA dataset experiments carried out on five groups,respectively,with Histograms of Oriented Gradients(HOG),Pyramid of Histograms of Orientation Gradients(PHOG),LBP,LGP detection rate,detection time,light and noise robustness contrast. Comprehensive various experimental data show that the robustness of illumination and noise is better.

Key words: two-dimensional discrete wavelet transform, pedestrian detection, Local Binary Pattern (LBP) feature, Local Gradient Pattern(LGP) feature, feature fusion, Support Vector Machine(SVM)