摘要
针对主成分分析算法将图像矩阵转化为向量的维数过高、求取特征向量耗时的问题,综合步态的静态和动态信息,对一个步态周期中的图像进行Radon变换,再通过模板构造,仅用一幅图像来刻画步态特征,接着用二维主成分分析(2DPCA)进行降维.为了验证所提出的算法的有效性,在CASIA步态数据库上进行实验,采用最近邻分类器来测试识别.实验结果表明在特征模板构造时选择合适的频率,采用Radon变换结合列2DPCA进行步态特征提取是有效的.
In the principal component analysis method,concatenating an image matrix often leads to a 1-D vector with high dimensionality,which makes it very difficult and time-consuming to compute the corresponding eigenvec-tors.By combining static components and dynamic information about the walking style,a novel gait representation was proposed.Gait characteristics were obtained from the Radon transform of gait sequences,where a single image could represent a person’s features by template construction.Then,two dimensional principal component analysis ( 2DPCA) was used to reduce the dimensions of training and testing data.The nearest neighbor classifier was em-ployed to distinguish the different gaits of human.We tested the proposed gait recognition method on the CASIA gait database.The experimental results demonstrated that,when frequency is chosen properly in template construc-tion,extraction of gait features using the Radon transform and column 2DPCA is very effective.
出处
《智能系统学报》
2010年第3期266-271,共6页
CAAI Transactions on Intelligent Systems
基金
国家"863"计划资助项目(2008AA01Z148)
黑龙江省杰出青年科学基金资助项目(JC200703)
哈尔滨市科技创新人才研究专项基金资助项目(2007RFXXG009)
关键词
步态识别
RADON变换
二维主成分分析
模板构造
gait recognition
Radon transform
two dimensional principal component analysis ( 2DPCA)
template construction