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改进的完全二维主成分分析及其在步态识别中的应用研究 被引量：5

Study on improved complete two dimensional principal component analysis and its application to gait recognition
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摘要 对完全二维主成分分析算法进行改进,提出三种不同的加权策略,详细地分析它们的本质,并将其应用到步态识别中。在中国科学院自动化所提供的CASIA(B)步态数据库下验证加权方案的有效性,实验结果表明加权幂指数的选取对识别结果的影响比较大,通过实验可以选取最佳的权值,能够做到提高识别性能。最后针对各个行走状态下的步态,分析了背包步态识别率低的原因。 This paper improved complete two dimensional principal component analysis,and proposed three different weighted strategies.It analyzed the essence of weighted scheme in detail.To evaluate the validity of the proposed method and apply it to gait recognition,it conducted experiments on CASIA（B） gait database.Experimental results show that the selection of weighted power exponent has great influence on the recognition result,and can improve the recognition performance by picking the best weight value in experiments.In the end,discussed the reason why the recognition rate of gait with a bag was low in the various walking states.
作者 贲晛烨 安实 王科俊 王健
机构地区 哈尔滨工业大学交通科学与工程学院 哈尔滨工程大学自动化学院
出处 《计算机应用研究》 CSCD 北大核心 2011年第6期2088-2091,共4页 Application Research of Computers
基金 国家"863"计划资助项目(2008AA01Z148)
关键词 步态识别 步态能量图 完全二维主成分分析 加权完全二维主成分分析 gait recognition gait energy image（GEI） complete two dimensional principal component analysis（C2DPCA） weighted complete two dimensional principal component analysis（WC2DPCA）
分类号 TP391.41 [自动化与计算机技术—计算机应用技术]
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参考文献20

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共引文献168

同被引文献56

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引证文献5

二级引证文献6

计算机应用研究
2011年 第6期

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