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Alternative TitleDynamic Bayesian Networks based Activity Recognition for Intelligent Space Systems
Thesis Advisor王飞跃
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控制理论与控制工程
Keyword智能空间 情景感知计算 动态贝叶斯网络 行为分析与识别 Intelligent Space Systems Context-aware Computing Dynamic Bayesian Networks Activity Recognition And Analysis
Abstract嵌入式计算、通信技术发展的结果促使与物理过程交互的网络化嵌入式系统的发展,由此而引发的"深度互联"的网络化系统促使智能空间系统的发展。智能空间系统中非常重要的是实现从网络化嵌入式系统采集的原始数据推理出高层的行动和目标,得出高层的语意信息。本文将以无线网络环境下构建智能空间原型系统,结合智能空间系统信息处理要求,研究支持智能空间系统情景感知(Context-Aware)要求的行为识别研究方法及其实现。本文将研究支持情景感知服务的智能空间行为识别研究框架,并研究实现算法,以及在试验平台上实现这些算法。本文的主要工作概述如下: (1)在原型系统实现方面,考虑到智能空间系统嵌入式计算、泛在网络的特定,我们开发了基于ZigBee协议的网络节点,并在次基础之上实现了覆盖范围广的室内无线网络;再次基础之上通过测量接收信号强度(RSS)的方式采集了关于用户在办公环境下的日常生活动作和行为的大量数据; (2)感知信号到位置状态的映射是智能空间系统情景感知的基础,考虑到室内无线信号的不确定性和噪音,我们采用率统计推理的方法进行位置计算,把位置计算问题转换为机器学习问题,并同过k-最近邻方法、多分类支持向量机方法、贝叶斯网络方法得到满意的位置计算精度和准确度; (3)在从原始数据推理出高层语意信息方面,考虑到嵌入式系统采集数据的噪音和用户行为动作的不确定性,我们采用基于层次化概率有向图模型的方法来建立智能空间情景感知计算的从传感器感知信息到高层动作和行为识别的模型;选择动态贝叶斯网络作为情景感知计算的推理引擎。考虑到动态贝叶斯网络中隐藏变量的存在,采用EM算法学习网络参数;以及基于联合树算法实现推理。为降低动态贝叶斯网络的计算复杂性,本文采用动作识别和行为识别分离的推理方法。动作识别由动态贝叶斯网络实现。行为识别通过两种算法实现:不考虑动作相关性的向量空间模型方法和考虑动作相关性的一阶马尔科夫 模型方法。最后通过在试验场景中采集的数据集上验证了基于动态贝叶斯网络方法动作和行为识别有效性。
Other AbstractRecent advances in embedded computing and wireless networking spur the emerging of wireless embedded computing systems that interact with the physical world. This trend has fostered growing effort towards Intelligent Space Systems (iSpaces) that embed intelligence into the physical spaces. One of the most important themes in iSpaces research is to infer high level activity information for the data collected via the wireless embedded computing systems. In this thesis, we will introduce the implement of an indoor iSpaces prototype system, discuss a framework for context-aware computing in iSpaces, and investigate algorithms dedicated to localization, and activity ecognition and analysis in iSpaces. The main work of this dissertation is as follows: (1) Considering the embedded computing and pervasive networking features of iSpaces, we developed wireless networking nodes based on ZigBee protocol; then we configure an wireless network that covers the whole indoor foor of the experimental environment, in which we collected huge number of data about activities about people therein via received signal strength; (2) Mapping the sensor signal into the position of the physical world is one of the fundamental steps towards context-aware computing in iSpaces. Considering the noises and uncertainty of the signal, we apply probabilistic inference methods to formulate the position computing problem into a machine learning problem. Algorithms based on k-Nearest Neighbors method, Multi-class Support Vector Machine method, and Bayesian networks method are applied to validate our methods. Experimental results show high accuracy and precision; (3) Considering the noise of the sensor signals and the uncertainty of user in iSpaces, we apply hierarchical probabilistic graphical method to model the relationship between sensor data, location, action and activity. Than we apply Dynamic Bayesian Networks (DBN) as the inference engine for activity recognition and analysis. Expectation-Maximization (EM) algorithm is adopted for parameter learning in that there are hidden variables in the DBN, and Junction Tree algorithm is applied for DBN inference. To reduce the complexity of parameter learning and inference in DBN, we partition the whole DBN model into two parts. At the low-level a DBN is to inference action sequences from sensor signals. At the high-level two methods are applied for activity recognition from the inferred action sequences. The first method is based on Vector Spa...
Other Identifier200628014628008
Document Type学位论文
Recommended Citation
GB/T 7714
刘斌. 基于动态贝叶斯网络的智能空间行为识别研究[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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