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Alternative TitleGesture Classification based on 3D Motion Capture Data
Thesis Advisor潘春洪
Degree Grantor中国科学院研究生院
Place of Conferral中国科学院自动化研究所
Degree Discipline模式识别与智能系统
Keyword运动捕捉 双手协调度 关键帧提取 运动周期性 分类树 Motion Capture Collaboration Between Arms Key Posture Extraction Motion Periodicity Classification Tree
Abstract二十世纪九十年代以来,随着运动捕捉技术的发展,大量的三维人体运动捕捉数据库被建立起来并广泛应用于手势识别的研究当中。正确高效的分析处理这些三维人体运动数据,对大规模三维运动捕捉数据库的开发和利用,从视频信息中提取出来的三维人体运动信息的后续应用都有着重要的意义。近年来基于运动捕捉数据的研究开始致力于在抽象层次上对运动数据进行语义分析。 本文主要利用运动捕捉的三维手势数据库,对日常交流的手势进行特征提取和语义的分类研究,主要工作包括: 1 采用运动捕捉的方法建立了三维手势数据库。我们利用运动捕捉设备采集了包含30个人的450个手势序列。该数据库主要针对人们日常交流中的双手手臂的动作,为日常会话手势的识别提供了很好的测试库。 2 开发了一个简易的人体骨架模型,该模型可对无效的运动数据进行修补并直观的显示捕捉的动作。为了便于数据的后续应用,将采集到的.trc数据格式转换成四元数的格式进行表达。 3 在特征提取方面,从底层特征出发,提出了一些新颖的具有语义信息的运动属性特征。通过对手势中人体部分之间的关系,手势的时空特征的分析,提取了双手的运动协调度,关键帧以及运动的周期性这三个运动属性特征。这些特征是连接传统的底层特征和高层语义之间的桥梁,为未来基于语义的手势识别提供良好的研究基础。 4 在分类器的构建方面,我们通过构建一个三层的分类树,把提取的有语义信息的运动属性特征融合在树的结构中。我们对不同的属性特征分别构建诸如GentleBoost,Nearest-Neighbour等单特征分类器,基于人们运动属性的一些先验,把分类的结果进行融合并形成一个树状的分类结构。
Other AbstractWith the popularity of Motion Capture Systems since 1990s,more and more 3D motion capture databases have been created and widely used in the research field of gesture recognition. How to effectively analyze motion capture data, is important to the organization and the re-use of large-scale motion capture database, as well as the follow-up application of 3D information extracted from videos. Recently, research on motion capture data has focused on how to analyze the semantic meaning of motion data. The work of this thesis is to explore new approaches to human arm gesture analysis based on the motion capture database, and present the following algorithms including extraction of gesture features and gesture classification. The thesis is organized as follows: 1. A gesture database is collected by using Motion Capture. The database, which is composed of 450 sequences of gestures made by 30 subjects, is mainly about communication gestures in our daily life, so that it can be a good test platform for communication gesture recognition. 2. Build a simple skeleton model, which can be used for the repair and display of motion capture data. For the follow-up application of motion data, .trc data format is transformed into quaternion to represent the gesture. 3. Some novel features that explicitly contain communicative information and have outstanding descriptive ability are extracted. Based on the analysis of human body parts and the spatial-temporal characteristic of gestures, collaboration between two arms, key posture, as well as the periodicity of gestures is extracted. These features function as a good way of building bridges between bottom-layer features and high-layer semantics. 4. A three-layer semantic classification tree is constructed by incorporating the semantic features on different layers by prior knowledge. Based on different features applied to different layers, classifiers like GentleBoost and Nearest Neighbor are built on different layers
Other Identifier200628014628039
Document Type学位论文
Recommended Citation
GB/T 7714
卢万平. 三维手势数据的语义分类[D]. 中国科学院自动化研究所. 中国科学院研究生院,2009.
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