关键词: 行人检测, 梯度直方图（HOG）, LGBPHS, HSC, 偏最小二乘法, SVM

Abstract: Based on the study of the applications of three different types of feature operators
in human detection, which are Histogram of Oriented Gradient (HOG), Local Gabor Binary
Pattern Histogram Sequence (LGBPHS) and Histogram of Shearlet Coefficients (HSC), we
combine them together and propose a new human detection feature operator. We employ Partial
Least Squares (PLS) analysis, an efficient dimensionality reduction technique, to project the
feature onto a much lower dimensional subspace. Using a linear SVM as the classifier, we
compare the fusion feature with the three single features in INRIA person dataset. Experiments
results shows we achieve a detection rate of 95.6% with FPPW=10-5.

Key words: human detection, HOG, LGBPHS, HSC, PLS, SVM