计算机工程2017，Vol.43Issue(3)：304-308,315,6.DOI:10.3969/j.issn.1000-3428.2017.03.051
一种模糊森林学习方法及其行人检测应用
A Fuzzy Forest Learning Method and Its Application of Pedestrian Detection
摘要
Abstract
To solve the over-fitting problem of Random Forest(RF) learning method for training data,a Fuzzy Forest(FF) learning method is proposed by improving the decision function of every decision node.Firstly,it builds decision function of every decision node on decision tree by using Gauss membership functions to convert the clear decision paths to fuzzy ones.Then,it produces fuzzy path with the product of the fuzzy decision values of all decision nodes which the sample goes through from root node to leaf node.Finally,it computes the prediction result according to each fuzzy path and prediction parameter of corresponding leaf node.The new FF learning method is applied in the field of pedestrian detection for learning and classifying both Haar and Histogram of Oriented Gradient(HOG) features.Experimental results show that FF is better than classical classifiers such as Adaboost,Support Vector Machine(SVM) and RF for improving the recognition rate of pedestrian detection.关键词
行人检测/随机森林/高斯隶属度函数/模糊决策/方向梯度直方图Key words
pedestrian detection/Random Forest(RF)/Gauss membership function/fuzzy decision/Histogram of Oriented Gradient(HOG)分类
信息技术与安全科学引用本文复制引用
周文谊,王吉源..一种模糊森林学习方法及其行人检测应用[J].计算机工程,2017,43(3):304-308,315,6.基金项目
江西省教育厅青年科学基金(GJJ14455). （GJJ14455）
