受限玻尔兹曼机的步态特征提取及其识别

doi:10.3969/j.issn.1000-1565.2019.06.014

河北大学学报(自然科学版) ›› 2019, Vol. 39 ›› Issue (6): 657-665.DOI: 10.3969/j.issn.1000-1565.2019.06.014

受限玻尔兹曼机的步态特征提取及其识别

李凯,曹可凡

收稿日期:2019-03-03 出版日期:2019-11-25 发布日期:2019-11-25
作者简介:李凯(1963—),男,河北保定人,河北大学教授,博士,主要从事机器学习、数据挖掘和模式识别的研究. E-mail:likai@hbu.edu.cn
基金资助:
河北省自然科学基金资助项目(F2018201060);河北大学研究生创新资助项目(hbu2019ss032)

Gait feature extraction based on restricted Boltzmann machine and its recognition method

LI Kai, CAO Kefan

School of Cyber Security and Computer, Hebei University, Baoding 071002, China

Received:2019-03-03 Online:2019-11-25 Published:2019-11-25

摘要/Abstract

摘要： 针对步态识别问题,研究了受限玻尔兹曼机的步态特征提取及其识别.主要基于行人图像序列,通过背景分割、归一化以及步态周期的计算,采用叠加方法生成步态能量图,以此作为步态的特征图像,利用受限玻尔兹曼机自动获取步态特征.选取中科院CASIA步态数据库以及支持向量机、孪生支持向量机、神经网络与K-近邻方法对使用受限玻尔兹曼机方法的特征提取进行了研究,同时与主成分分析PCA、线性判别分析LDA、卷积神经网络CNN特征提取与识别方法进行了比较.

关键词: 受限玻尔兹曼机(RBM), 步态能量图, 特征提取, 步态识别

Abstract: Aiming at gait recognition, the gait feature extraction and recognition of restricted Boltzmann machine(RBM)are studied. Based on the human pedestrian image sequence, the gait energy map is generated by background segmentation, normalization and gait cycle calculation. The gait energy map is used as the feature image of gait, and the gait feature is automatically acquired by RBM. The gait database of Chinese Academy of Sciences CASIA is chosen to study the gait recognition using RBM method for different classification methods, including support vector machine, twin support vector machine, neural network and K-nearest neighbor method. At the same time, this approach is compared with PCA, LDA and CNN feature extraction and recognition.

Key words: restricted Boltzmann machine(RBM), gait energy map, feature extraction, gait recognition

中图分类号:

TP391

李凯,曹可凡. 受限玻尔兹曼机的步态特征提取及其识别[J]. 河北大学学报(自然科学版), 2019, 39(6): 657-665.

LI Kai, CAO Kefan. Gait feature extraction based on restricted Boltzmann machine and its recognition method[J]. Journal of Hebei University (Natural Science Edition), 2019, 39(6): 657-665.

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受限玻尔兹曼机的步态特征提取及其识别

Gait feature extraction based on restricted Boltzmann machine and its recognition method

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