文献类型：期刊文献

中文题名：基于Boosting RBF神经网络的人体行为识别

英文题名：Recognition of human action using boosting method and RBF neural network

作者：叶银兰[1]

机构：[1]绍兴文理学院上虞分院

年份：2008

卷号：44

期号：13

起止页码：188

中文期刊名：计算机工程与应用

外文期刊名：Computer Engineering and Applications

收录：CSTPCD、、北大核心2004、CSCD2011_2012、北大核心、CSCD

基金：国家高技术研究发展计划(863)(the National High-Tech Research and Development Plan of China under Grant No.2005AA414010);浙江省自然科学基金(the Natural Science Foundation of Zhejiang Province of China under Grant No.M603034)

语种：中文

中文关键词：Zernike矩;人体行为识别;boosting算法;运动历史图像

外文关键词：Zernike moments;recognition of human action;Boosting method;motion history image

中文摘要：提出一种基于BoostingRBF神经网络的人体行为识别方法,该方法利用规范化的运动历史图像(MHI)进行图像序列表示,从中提取Zernike矩的统计描述特征,然后提出Adaboost算法自适应地选择图像序列的特征作为RBF神经网络的输入,为了进一步提高神经网络的泛化能力,采用一种调整权值分布,限制权重扩张的改进的Boosting方法,分类器以加权投票方式进行分类决策。实验结果表明,提出的方法能够有效地识别人体运动类别。

外文摘要：A novel method is proposed for recognition of human action based on improved boosting RBF neural network.In the proposed method,normalized motion history image for motion representation is valued.Statistical descriptions are then computed from motion history image using Zernike moment-based features,then adaboost method is proposed for adaptive select feature. Then RBF neural network is used to class the human action.In order to improve the precision of the RBF neural network for recognition of human action,a weight-adjusting-based method is proposed to improve Boosting method.Experiment results have shown good recognition performance of our method.