一种步态识别方法

步态识别作为一种新的生物特征识别技术,通过人走路的姿势实现对个人身份的识别和认证.算法利用步态轮廓图像边界到重心的距离矢量对步态轮廓图像进行描述,采用步态图像的高宽比进行步态的准周期性分析.利用隐马尔可夫模型进行步态时变数据匹配识别.算法在CMU数据库上面进行实验取得了较高的正确识别率.     

一种新的步态图像序列分割算法

在运动目标步态识别中,从步态图像序列中提取出完整的人体运动轮廓对特征提取、目标分类和目标识别等有着非常重要的意义。提出了一种新的运动目标分割算法：首先应用改进的块匹配算法进行运动估计;然后运用分水岭算法把当前帧图像分割成许多封闭而不重叠的小区域;最后运用仿射参数模型进行运动块区域合并。在CMU步态数据库中采用基准算法进行的实验表明,运用所提出的算法能够提取出完整的人体轮廓,进一步提高步态识别的识别率。     

基于步态识别的远距离身份认证

通过人走路的姿势实现对个人身份的远距离识别和认证是当前生物特征识别研究领域的一个研究热点。算法利用步态轮廓图像边界到重心的距离矢量对步态轮廓图像进行人体运动的静态形状描述,采用连续隐马尔可夫模型对人体运动时从一个动作到另一个动作的过渡进行动态描述。算法在CMU数据库上面进行实验取得了较高的正确识别率。     

基于连续隐马尔可夫模型的步态识别

步态识别作为一种新的生物特征识别技术,通过人走路的姿势实现对个人身份的识别和认证.算法利用步态轮廓图像边界到重心的距离矢量对步态轮廓图像进行描述,采用步态图像的高宽比进行步态的准周期性分析.利用隐马尔可夫模型进行步态时变数据匹配识别.算法在CMU数据库上进行实验取得了较高的正确识别率.     

Improved gait recognition by gait dynamics normalization

Potential sources for gait biometrics can be seen to derive from two aspects: gait shape and gait dynamics. We show that improved gait recognition can be achieved after normalization of dynamics and focusing on the shape information. We normalize for gait dynamics using a generic walking model, as captured by a population Hidden Markov Model (pHMM) defined for a set of individuals. The states of this pHMM represent gait stances over one gait cycle and the observations are the silhouettes of the corresponding gait stances. For each sequence, we first use Viterbi decoding of the gait dynamics to arrive at one dynamics-normalized, averaged, gait cycle of fixed length. The distance between two sequences is the distance between the two corresponding dynamics-normalized gait cycles, which we quantify by the sum of the distances between the corresponding gait stances. Distances between two silhouettes from the same generic gait stance are computed in the linear discriminant analysis space so as to maximize the discrimination between persons, while minimizing the variations of the same subject under different conditions. The distance computation is constructed so that it is invariant to dilations and erosions of the silhouettes. This helps us handle variations in silhouette shape that can occur with changing imaging conditions. We present results on three different, publicly available, data sets. First, we consider the HumanlD Gait Challenge data set, which is the largest gait benchmarking data set that is available (122 subjects), exercising five different factors, i.e., viewpoint, shoe, surface, carrying condition, and time. We significantly improve the performance across the hard experiments involving surface change and briefcase carrying conditions. Second, we also show improved performance on the UMD gait data set that exercises time variations for 55 subjects. Third, on the CMU Mobo data set, we show results for matching across different walking speeds. It is worth noting that there was no separate training for the UMD and CMU data sets.     

基于傅立叶描绘子的步态识别

步态识别作为一种新的生物特征识别技术，通过人走路的姿势实现对个人身份的识别和认证。利用傅立叶描绘子对步态轮廓图像进行描述，用步态图像的高宽比进行步态的准周期性分析，并采用动态时间规正算法解决不同的步态周期的图像序列之间的比较问题。该算法在CMU数据库上面进行试验取得了较高的正确识别率。     

基于步态序列图像的身份确认

为了利用HMM抽取的步态序列的动态特征来进行身份确认，首先提出一种改进的角度向量用来表征二值化的步态序列图像，以便将每幅图像转化为1维向量，然后再以此作为特征向量，对每个人物建立并训练HMM模型，用于确定人物身份。这种改进的角度向量由于具有较强的抗噪性和方便的尺度伸缩性能，因此既适用于分割质量较差的图像，又能减小行走方向和距离的影响。实验表明，这种HMM不仅能较好地模拟步态的动态特征，还能描述序列图像间的联系，而且算法执行速度快，从输入原始数据到输出识别结果所需时间不超过2min，能满足实时要求。在Soton和NLPR数据库上进行的实验，分别获得了100％和85％的识别率，证明该方法是有效的。     

基于HMM与遗传神经网络的改进语音识别系统

为了解决语音信号中帧与帧之间的重叠,提高语音信号的自适应能力,本文提出基于隐马尔可夫(HMM)与遗传算法神经网络改进的语音识别系统.该改进方法主要利用小波神经网络对Mel频率倒谱系数(MFCC)进行训练,然后利用HMM对语音信号进行时序建模,计算出语音对HMM的输出概率的评分,结果作为遗传神经网络的输入,即得语音的分类识别信息.实验结果表明,改进的语音识别系统比单纯的HMM有更好的噪声鲁棒性,提高了语音识别系统的性能.     

基于HMM的动态社会网络社团发现算法

随着互联网的不断发展,大多数社会网络已逐渐显示出动态特性,动态社会网络社团分析对理解现实生活中社会网络结构和功能具有非常重要的意义.针对动态社会网络中的社团发现问题,提出一种基于隐Markov模型(hidden Markov model, HMM)的HMM_DC算法.该算法考虑到社会网络的动态特性,结合历史信息,将社团发现转化为求解隐马尔可夫模型中的最优状态序列问题,将网络中的社团结构和节点信息分别采用状态链和观察链表示,在无须指定额外参数的情况下实现动态网络的社团结构发现.最后,利用该算法和其他算法对VAST数据集、ENRON数据集和Facebook social network数据集进行实验仿真.仿真结果表明：该算法能够快速、准确地发现真实动态网络中的社团,其模块度Q值和互信息NMI值有很大提升.     

基于DBN的特种车辆前向防撞推理模型

针对特种车辆在动态环境中的前向碰撞风险评估问题,对特种车辆前向碰撞风险的自然因素、驾驶员行为特征等进行研究,并对固定的车辆安全防撞距离阈值进行改进,提出了一种基于动态贝叶斯网络的前向防撞推理模型。该模型将自车与周围环境的位置关系、环境关系、驾驶员行为等因素进行融合,一旦周围环境发生变化,该模型可以及时评估前向风险,并与静态贝叶斯网络的前向推理模型进行对比分析。仿真实验验证了该前向防撞推理模型的可行性和有效性。     

基于改进蛇模型的步态轮廓提取

提出了一种基于Snake模型的改进算法，不仅能够精确地搜索到图像轮廓，且程序运行速度较快。该算法在CMU数据库上进行了实验，结果表明提取出的步态轮廓完整且封闭，能有效地提高识别率。     

变结构动态贝叶斯网络的机制研究

传统的动态贝叶斯网络(Dynamic Bayesian networks, DBNs)描述的是一个稳态过程,而处理非稳态过程,变结构动态贝叶斯网络更适 用、更灵活、更有效.为了克服现有变结构离散 动态贝叶斯网络推理算法只能处理硬证据的缺陷,本文在深入分析变结构动态贝叶斯网络机制及其特 征的基础上,提出了变结构离散动态贝叶斯网络的 快速推理算法.此外,对变结构动态贝叶斯网络的特例,即数据缺失动态贝叶斯网络进行了定义并构建 了相应的模型.仿真实验验证了变结构离散动态贝 叶斯网络快速推理算法的有效性及计算效率.     

An HMM approach for optimal investment of an insurer

In this paper, we introduce a Hidden Markov Model (HMM) for studying an optimal investment problem of an insurer when model uncertainty is present. More specifically, the financial price and insurance risk processes are modulated by a continuous‐time, finite‐state, hidden Markov chain. The states of the chain represent different modes of the model. The HMM approach is viewed as a ‘dynamic’ version of the Bayesian approach to model uncertainty. The optimal investment problem is formulated as a stochastic optimal control problem with partial observations. The innovations approach in the filtering theory is then used to transform the problem into one with complete observations. New robust filters of the chain and estimates of key parameters are derived. We discuss the optimal investment problem using the Hamilton–Jacobi–Bellman (HJB) dynamic programming approach and derive a closed‐form solution in the case of an exponential utility and zero interest rate. Copyright © 2011 John Wiley & Sons, Ltd.     

可量化的移动Ad Hoc 网络时空动态特性评估方法

移动模型是Ad Hoc网络区别于其他形式网络的重要标志,对其产生的动态网络特性(简称动态特性)进行评估,是研究Ad Hoc网络的协议仿真和网络相关技术(如拓扑控制和网络性能测量等)的基础性问题.在已有研究的基础上,改进了网络的模型化描述,克服了以往模型无法很好地描述相关联的时空动态特性的缺陷,并在此基础上,提出了移动模型通用的可量化时空动态特性评估方法.通过构建节点空间位置分布,建立网络拓扑时空动态特性的分析模型,深入研究了几种移动模型的动态性.提出一种圆周曲线移动模型,弥补了以往移动模型难以描述现实的曲线移动场景.仿真实验结果表明,该方法能够有效地对现有移动模型的动态性进行评估.实验结果表明,圆周曲线移动模型与以往移动模型相比,具有良好的时空动态特性.     

基于HMM的步态身份识别

随着生物识别悄然兴起,生物识别技术逐渐成为新的身份识别技术。步态识别是生物特征识别技术的一个新兴子领域。文章就是将隐马尔可夫模型(HMM,HiddenMarkovModel)方法运用在步态身份识别中,并进行了其识别性能的研究。该文给出了一个基于HMM的步态身份识别方案,并进行了图像预处理,HMM参数训练和识别的研究,得出了一些有意义的结论。同时在中国科学院自动化研究所提供的CASIA步态数据库上进行了步态身份识别实验,实验结果表明:在侧面视角下采用此方法,具有较好的识别率。     

A non-homogeneous dynamic Bayesian network with a hidden Markov model dependency structure among the temporal data points

In the topical field of systems biology there is considerable interest in learning regulatory networks, and various probabilistic machine learning methods have been proposed to this end. Popular approaches include non-homogeneous dynamic Bayesian networks (DBNs), which can be employed to model time-varying regulatory processes. Almost all non-homogeneous DBNs that have been proposed in the literature follow the same paradigm and relax the homogeneity assumption by complementing the standard homogeneous DBN with a multiple changepoint process. Each time series segment defined by two demarcating changepoints is associated with separate interactions, and in this way the regulatory relationships are allowed to vary over time. However, the configuration space of the data segmentations (allocations) that can be obtained by changepoints is restricted. A complementary paradigm is to combine DBNs with mixture models, which allow for free allocations of the data points to mixture components. But this extension of the configuration space comes with the disadvantage that the temporal order of the data points can no longer be taken into account. In this paper I present a novel non-homogeneous DBN model, which can be seen as a consensus between the free allocation mixture DBN model and the changepoint-segmented DBN model. The key idea is to assume that the underlying allocation of the temporal data points follows a Hidden Markov model (HMM). The novel HMM–DBN model takes the temporal structure of the time series into account without putting a restriction onto the configuration space of the data point allocations. I define the novel HMM–DBN model and the competing models such that the regulatory network structure is kept fixed among components, while the network interaction parameters are allowed to vary, and I show how the novel HMM–DBN model can be inferred with Markov Chain Monte Carlo (MCMC) simulations. For the new HMM–DBN model I also present two new pairs of MCMC moves, which can be incorporated into the recently proposed allocation sampler for mixture models to improve convergence of the MCMC simulations. In an extensive comparative evaluation study I systematically compare the performance of the proposed HMM–DBN model with the performances of the competing DBN models in a reverse engineering context, where the objective is to learn the structure of a network from temporal network data.     

基于HMM-FNN模型的复杂动态手势识别

复杂动态手势识别是利用视频手势进行人机交互的关键问题.提出一种HMM-FNN模型结构.它整合了隐马尔可夫模型对时序数据的建模能力与模糊神经网络的模糊规则构建与推理能力,并将其应用到复杂动态手势的识别中.复杂动态手势具备两大特点:运动特征的可分解性与定义描述的模糊性.针对这两种特性,复杂手势被分解为手形变化、2D平面运动与Z轴方向运动3个子部分,分别利用HMM进行建模,HMM模型对观察子序列的似然概率被作为FNN的模糊隶属度,通过模糊规则推理,最终得到手势的分类类别.HMM-FNN方法将高维手势特征分解为低维子特征序列,降低了模型的复杂度.此外,它还可以充分利用人的经验辅助模型结构的创建与优化.实验表明,该方法是一种有效的复杂动态手势识别方法,并且优于传统的HMM模型方法.     

CONSTRUCTION OF BELIEF AND DECISION NETWORKS

We describe a representation and set of inference techniques for the dynamic construction of probabilistic and decision-theoretic models expressed as networks. In contrast to probabilistic reasoning schemes that rely on fixed models, we develop a representation that implicitly encodes a large number of possible model structures. Based on a particular query and state of information, the system constructs a customized belief net for that particular situation. We develop an interpretation of the network construction process in terms of the implicit networks encoded in the database. A companion method for constructing belief networks with decisions and values (decision networks) is also developed that uses sensitivity analysis to focus the model building process. Finally, we discuss some issues of control of model construction and describe examples of constructing networks.     

一种基于CSI的高鲁棒性步态识别方法

针对目前的室内人员步态识别方法存在计算量大、设备成本高、鲁棒性低等问题,提出一种基于信道状态信息的高鲁棒性室内人员步态识别方法WiKown。通过快速傅里叶变换设置能量指示器监测人员行走行为,将采集的CSI步态数据经滤波与降噪处理后以滑动窗口的方式提取特征值,得到人员步态的CSI信息后建立观测序列,最后通过高斯分布叠加拟合后引入隐马尔科夫模型计算观测序列概率,生成步态参数模型。在走廊、实验室和大厅真实多人环境中,WiKown方法对单人步态的平均识别率达到92.71%。实验结果表明,与决策树、动态时间规整和长短时记忆网络方法相比较,该方法能有效地识别出人员的步态信息,提升了识别精度和鲁棒性。