基于视觉的步态识别研究综述
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摘要
步态识别具有易采集,远距离,非接触,难伪装和非侵入性等优势,是生物特征识别、计算机视觉和信息安全等领域的研究热点.本文主要从人体检测,步态周期性,特征提取,识别分类四个方面综述了步态识别的各种方法,其中人体检测部分主要介绍了当前步态数据库和前景分割两部分,并分析了阴影的去除方法;接下来介绍步态周期性和其他步态常用参数数据;之后分类介绍了特征提取部分;识别分类以模板匹配法、基于支持向量机的方法、基于人工神经网络的方法、统计学方法及其他常用方法五部分进行介绍.最后总结了步态识别已解决的难点,并对步态识别的发展趋势作出分析,指出未来步态识别的发展方向.
Gait recognition has many advantages,such as easy acquisition,long distance,non-contact,hard to disguise and non-invasive. It is the focus of research in biometrics,computer vision and information security. This paper mainly summarizes the methods of gait recognition from four aspects:human body detection,gait periodicity,feature extraction,recognition and classification. The human detection part mainly introduces the current gait databases and foreground segmentation in two parts,and analyzes the method of shadowremoval;Next,this paper introduces the data of gait periodicity and other gait parameters;Then the feature extraction part is introduced;The identification and classification are described in five parts:template matching method,support vector machine(SVM) method,artificial neural network method,statistical method and other common methods. In the end,the solved difficulties of gait recognition are summarized,and the development trend of gait recognition is analyzed,and the future development direction of gait recognition is pointed out.
Gait recognition has many advantages,such as easy acquisition,long distance,non-contact,hard to disguise and non-invasive. It is the focus of research in biometrics,computer vision and information security. This paper mainly summarizes the methods of gait recognition from four aspects:human body detection,gait periodicity,feature extraction,recognition and classification. The human detection part mainly introduces the current gait databases and foreground segmentation in two parts,and analyzes the method of shadowremoval;Next,this paper introduces the data of gait periodicity and other gait parameters;Then the feature extraction part is introduced;The identification and classification are described in five parts:template matching method,support vector machine(SVM) method,artificial neural network method,statistical method and other common methods. In the end,the solved difficulties of gait recognition are summarized,and the development trend of gait recognition is analyzed,and the future development direction of gait recognition is pointed out.
引文
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[19]Ma G,Wu L,Wang Y.A general subspace ensemble learning framew ork via totally-corrective boosting and tensor-based and local patch-based extensions for gait recognition[J].Pattern Recognition,2017,66(6):280-294.
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