《计算机应用研究》|Application Research of Computers

基于WM-CoSaMP重构算法的压缩感知在步态识别中的应用研究

Compressed sensing for gait recognition with WM-CoSaMP

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作者 苏维均,李明星,于重重,王红红
机构 北京工商大学 计算机与信息工程学院,北京 100048
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文章编号 1001-3695(2015)01-0291-04
DOI 10.3969/j.issn.1001-3695.2015.01.068
摘要 针对步态识别中步态特征提取高维处理的复杂性,在研究压缩感知理论的基础上,提出将压缩感知理论应用于步态识别中的步态特征提取方面。在充分利用步态图像稀疏性的前提下,利用观测矩阵对步态图像进行投影观测,得到的观测值作为步态特征用于步态识别中,实现了特征提取的降维处理,大大降低了计算的复杂性。在步态图像的重构方面,在压缩采样匹配追踪(CoSaMP)的基础上,提出了基于小波树模型的压缩采样匹配(wavelet model-CoSaMP,WM-CoSaMP)的重构算法,进一步提高了重构精度。通过对比实验,验证了WM-CoSaMP重构算法的优越性,以及压缩感知在步态特征提取方面的优越性。
关键词 步态识别;特征提取;压缩感知;投影观测;重构;基于小波树模型的压缩采样匹配(WM-CoSaMP)
基金项目 北京市自然科学基金重点项目(KZ201410011014)
北京市学科建设资助项目(PXM2012_014213_0000_74)
北京市教委科技面上资助项目(Km201110011006)
本文URL http://www.arocmag.com/article/01-2015-01-068.html
英文标题 Compressed sensing for gait recognition with WM-CoSaMP
作者英文名 SU Wei-jun, LI Ming-xing, YU Chong-chong, WANG Hong-hong
机构英文名 College of Computer & Information Engineering, Beijing Technology & Business University, Beijing 100048, China
英文摘要 There are some problems in the gait recognition, especially for the complexity of the high dimension features. Based on the study of compressed sensing theory, this paper proposed that compressed sensing was used as a new paradigm for gait feature extraction. While the gait image had sparse representation in some orthonormal basis, projections of the images, which were taken as the gait features could be gotten by the projection matrix. In the reconstruction of the gait image, this paper proposed that the WM-CoSaMP based on the wavelet tree model could be used as the recovery algorithm, which could further improve the precision of the reconstruction. Experiments show that the WM-CoSaMP outperforms the OMP and the CoSaMP. And other experiments demonstrate that the application of compressed sensing in the gait feature extraction performs better than the PCA and MPCA.
英文关键词 gait recognition; feature extraction; compressed sensing; projection; reconstruction; WM-CoSaMP
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收稿日期 2013/12/9
修回日期 2014/1/26
页码 291-294
中图分类号 TP391.41
文献标志码 A