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基于步态动态图的步态识别方法

发表时间:2017-08-31  浏览量:159  下载量:19
全部作者: 周磊,叶俊勇
作者单位: 重庆大学光电技术及系统教育部重点实验室
摘 要: 为有效地从多帧动态步态序列中提取步态特征并进行身份鉴别,采用一种基于步态动态图(gait dynamic image,GDI)的步态特征提取方法,主要通过排序池化(rank pooling,RP)方法对步态序列的动态信息进行编码。将排序池化方法作用于步态序列,生成对应的步态动态图;然后使用核主成分分析(kernel principal component analysis,KPCA)方法提取步态动态图的主成分,其可以对图像像素间的相关性进行描述;最后将主成分与泛化能力较强的支持向量机(support vector machine,SVM)分类器相结合进行步态识别。实验结果表明,本文方法取得了较理想的识别效果。
关 键 词: 图像处理;步态识别;步态动态图;排序池化;核主成分分析;支持向量机
Title: Gait recognition method based on gait dynamic image
Author: ZHOU Lei, YE Junyong
Organization: Key Laboratory of Optoelectronic Technology and Systems, Ministry of Education, Chongqing University
Abstract: In order to extract gait features more efficiently from a sequence of multi-frame dynamic gaits and identity recognition, a novel approach to extract gait features based on gait dynamic image (GDI) was proposed, which encodes dynamics of a gait sequence by rank pooling (RP) method. Gait dynamic images were obtained by directly applying rank pooling on gait sequences. Kernel principal component analysis (KPCA) extracted principal components from gait dynamic images with nonlinear method, and described the relationship among three or more pixels of the identified images. The support vector machine (SVM) classifier is adopted based on its excellent generalization capacity in classification problem to gait recognition, combined with principal component analysis. Experimental results show that the method is capable of achieving excellent recognition performance.
Key words: image processing; gait recognition; gait dynamic image; rank pooling; kernel principal component analysis; support vector machine
发表期数: 2017年8月第16期
引用格式: 周磊,叶俊勇. 基于步态动态图的步态识别方法[J]. 中国科技论文在线精品论文,2017,10(16):1811-1818.
 
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