计算机工程2012，Vol.38Issue(13)：5-8,4.DOI:10.3969/j.issn.1000-3428.2012.13.002
基于随机森林的目标检测与定位
Object Detection and Localization Based on Random Forest
摘要
Abstract
In order to solve the object detection and localization in the complicated image, this paper presents an algorithm for object detection and localization based on random forest. The Scale Invariant Feature Transform(SIFT) local features are used to construct a random forest classifier. A tree-structured discriminative codebook model is constructed by all leaf nodes of a decision tree. The discriminative codebook is used to estimate the object's location via a probabilistic computation called probabilistic Hough vote. Experimental results show that the proposed algorithm has higher efficiency, and can provide a better detection results in a complicated environment.关键词
随机森林/结构模型学习/SIFT局部特征/判别式码本模型/概率Hough投票/目标遮挡Key words
random forest/ structural model learning/ Scale Invariant Feature Transform(SIFT) local feature/ discriminative codebook model/probabilistic Hough vote/ object occlusion分类
信息技术与安全科学引用本文复制引用
刘足华,熊惠霖..基于随机森林的目标检测与定位[J].计算机工程,2012,38(13):5-8,4.基金项目
国家自然科学基金资助项目(60775008,61075106) （60775008,61075106）
