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1. 发明授权

US06683968B1 Method for visual tracking using switching linear dynamic system models 失效
公开(公告)号：US06683968B1
公开(公告)日：2004-01-27
申请号：US09654022
申请日：2000-09-01
申请人： Vladimir Pavlović , James Matthew Rehg
发明人： Vladimir Pavlović , James Matthew Rehg
IPC分类号： G06K900
CPC分类号： G06K9/6297 , G06K9/00335
摘要： A target in a sequence of measurements is tracked by modeling the target with a switching linear dynamic system (SLDS) having a plurality of dynamic models. Each dynamic model is associated with a switching state such that a model is selected when its associated switching state is true. A set of continuous state estimates is determined for a given measurement, and for each possible switching state. A state transition record is then determined by determining and recording, for a given measurement and for each possible switching state, an optimal previous switching state, based on the measurement sequence, where the optimal previous switching state optimizes a transition probability based on the set of continuous state estimates. A measurement model of the target is fitted to the measurement sequence. The measurement model is the description of the influence of the state on the measurement. It couples what is observed to the estimated target. Finally, a trajectory of the target is estimated from the measurement model fitting, the state transition record and parameters of the SLDS, where the estimated trajectory is a sequence of continuous state estimates of the target which correspond to the measurement sequence. The set of continuous state estimates is preferably obtained through Viterbi prediction. The optimal previous switching state can be an optimal prior switching state, or can be an optimal posterior switching state.

2. 发明授权

US06694044B1 Method for motion classification using switching linear dynamic system models 失效
公开(公告)号：US06694044B1
公开(公告)日：2004-02-17
申请号：US09654300
申请日：2000-09-01
申请人： Vladimir Pavlović , James Matthew Rehg
发明人： Vladimir Pavlović , James Matthew Rehg
IPC分类号： G06K900
CPC分类号： G05B17/02 , G06K9/00335
摘要： Portions of an input measurement sequence are classified into a plurality of regimes by associating each of a plurality of dynamic models with one a switching state such that a model is selected when its associated switching state is true. In a Viterbi-based method, a state transition record is determined, based on the input sequence. A switching state sequence is determined by backtracking through the state transition record. Finally, portions of the input sequence are classified into different regimes, responsive to the switching state sequence. In a variational-based method, the switching state at a particular instance is also determined by a switching model. The dynamic model is then decoupled from the switching model. Parameters of the decoupled dynamic model are determined responsive to a switching state probability estimate. A state of the decoupled dynamic model corresponding to a measurement at the particular instance is estimated, responsive to the input sequence. Parameters of the decoupled switching model are then determined responsive to the dynamic state estimate. A probability is estimated for each possible switching state of the decoupled switching model. A switching state sequence is determined based on the estimated switching state probabilities. Finally, portions of the input sequence are classified into different regimes, responsive to the determined switching state sequence.

3. 发明授权

US06591146B1 Method for learning switching linear dynamic system models from data 失效
标题翻译：从数据学习切换线性动态系统模型的方法
公开(公告)号：US06591146B1
公开(公告)日：2003-07-08
申请号：US09654426
申请日：2000-09-01
申请人： Vladimir Pavlović , James Matthew Rehg
发明人： Vladimir Pavlović , James Matthew Rehg
IPC分类号： G05B1302
CPC分类号： G06K9/6297 , G06K9/00335 , G06N99/005
摘要： From a set of possible switching states and responsive to a sequence of measurements, a corresponding sequence of switching states is determined for a system having a plurality of dynamic models, associates each model with a switching state such that a model is selected when its associated switching state is true. A state transition record is determined, based on the measurement sequence. The sequence of switching states is determined by backtracking through the state transition record. Alternatively, the switching state model is decoupled from the dynamic system model. The decoupled switching state model is transformed into a hidden Markov model (HMM) switching state model, while the decoupled dynamic system model is transformed into a time-varying dynamic system model. A solution to the dynamic system model is estimated using a Kalman filter. Next, variational parameters of the HMM switching state model are determined based on the estimated-solution, where the variational parameters measure an agreement of each model from the plurality of dynamic models with the solution. A sequence of switching states for the HMM switching state model is then determined based on the variational parameters of the HMM switching state model. finally, variational parameters of the dynamic system model are determined based on the determined sequence of switching states, such that the variational parameters are proportional to a combination of model parameters form the plurality of dynamic models weighted by the probability of the switching states.
摘要翻译：从一组可能的切换状态并响应于测量序列，确定具有多个动态模型的系统的相应的切换状态序列，将每个模型与切换状态相关联，使得当其相关切换状态是真实的。基于测量序列确定状态转换记录。切换状态的顺序通过状态转换记录的回溯来确定。或者，切换状态模型与动态系统模型分离。解耦开关状态模型被转换为隐马尔可夫模型（HMM）切换状态模型，而解耦动态系统模型则转化为时变动态系统模型。使用卡尔曼滤波器估计动态系统模型的解决方案。接下来，基于所估计的解决方案来确定HMM切换状态模型的变分参数，其中变化参数利用解决方案测量来自多个动态模型的每个模型的一致性。然后基于HMM切换状态模型的变化参数来确定用于HMM切换状态模型的切换状态的序列。最后，基于确定的切换状态序列来确定动态系统模型的变分参数，使得变分参数与由切换状态的概率加权的多个动态模型的模型参数的组合成比例。

4. 发明授权

US06993462B1 Method for motion synthesis and interpolation using switching linear dynamic system models 失效
标题翻译：使用开关线性动态系统模型的运动合成和插值方法
公开(公告)号：US06993462B1
公开(公告)日：2006-01-31
申请号：US09654401
申请日：2000-09-01
申请人： Vladimir Pavlović , James Matthew Rehg
发明人： Vladimir Pavlović , James Matthew Rehg
IPC分类号： G06F17/10
CPC分类号： G06K9/00342 , G06K9/6297 , G06T7/20 , G06T13/40
摘要： A method for synthesizing a sequence includes defining a switching linear dynamic system (SLDS) with a plurality of dynamic systems. In a Viterbi-based method, a state transition record for a training sequence is determined. The corresponding sequence of switching states is determined by backtracking through the state transition record. Parameters of dynamic models are learned in response to the determined sequence of switching states, and a new data sequence is synthesized, based on the dynamic models whose parameters have been learned. In a variational-based method, the switching state at a particular instance is determined by a switching model. The dynamic models are decoupled from the switching model, and parameters of the decoupled dynamic model are determined responsive to a switching state probability estimate. Similar methods are used to interpolate from an input sequence.
摘要翻译：一种用于合成序列的方法包括用多个动态系统定义切换线性动态系统（SLDS）。在基于维特比的方法中，确定训练序列的状态转换记录。通过状态转换记录的回溯确定相应的切换状态序列。根据确定的切换状态顺序，学习动态模型的参数，并根据已经学习参数的动态模型，合成新的数据序列。在基于变分的方法中，特定情况下的切换状态由切换模型确定。动态模型与交换模型分离，解耦动态模型的参数是响应于开关状态概率估计而确定的。类似的方法用于从输入序列内插。

5. 发明授权

US06944317B2 Method for motion classification using switching linear dynamic systems models 有权
标题翻译：使用切换线性动态系统模型的运动分类方法
公开(公告)号：US06944317B2
公开(公告)日：2005-09-13
申请号：US10663938
申请日：2003-09-16
申请人： Vladimir Pavlović , James Matthew Rehg
发明人： Vladimir Pavlović , James Matthew Rehg
IPC分类号： G05B17/02 , G06K9/00
CPC分类号： G05B17/02 , G06K9/00335
摘要： Portions of an input measurement sequence are classified into a plurality of regimes by associating each of a plurality of dynamic models with one a switching state such that a model is selected when its associated switching state is true. In a Viterbi-based method, a state transition record is determined, based on the input sequence. A switching state sequence is determined by backtracking through the state transition record. Finally, portions of the input sequence are classified into different regimes, responsive to the switching state sequence. In a variational-based method, the switching state at a particular instance is also determined by a switching model. The dynamic model is then decoupled from the switching model. Parameters of the decoupled dynamic model are determined responsive to a switching state probability estimate. A state of the decoupled dynamic model corresponding to a measurement at the particular instance is estimated, responsive to the input sequence. Parameters of the decoupled switching model are then determined responsive to the dynamic state estimate. A probability is estimated for each possible switching state of the decoupled switching model. A switching state sequence is determined based on the estimated switching state probabilities. Finally, portions of the input sequence are classified into different regimes, responsive to the determined switching state sequence.
摘要翻译：通过将多个动态模型中的每一个与一个切换状态相关联使得在其相关联的切换状态为真时选择模型，将输入测量序列的部分分类为多个方案。在基于维特比的方法中，基于输入序列确定状态转移记录。通过状态转换记录的回溯来确定切换状态序列。最后，响应于切换状态序列，输入序列的部分被分类成不同的方式。在基于变分的方法中，特定情况下的切换状态也由切换模型确定。然后将动态模型与交换模型分离。响应于切换状态概率估计确定解耦动态模型的参数。响应于输入序列，估计对应于特定情况下的测量的解耦动态模型的状态。然后响应于动态状态估计来确定去耦开关模型的参数。估计解耦开关模型的每个可能切换状态的概率。基于估计的切换状态概率来确定切换状态序列。最后，响应于所确定的切换状态序列，输入序列的部分被分类成不同的方式。

6. 发明授权

US06243106B1 Method for figure tracking using 2-D registration and 3-D reconstruction 失效
标题翻译：使用二维配准和三维重建的图形跟踪方法
公开(公告)号：US06243106B1
公开(公告)日：2001-06-05
申请号：US09059197
申请日：1998-04-13
申请人： James Matthew Rehg , Daniel D. Morris
发明人： James Matthew Rehg , Daniel D. Morris
IPC分类号： G06K900
CPC分类号： G06K9/00369 , G06T7/246
摘要： In a computerized method, a moving articulated figure is tracked in a sequence of 2-D images measured by a monocular camera. The images are individually registered with each other using a 2-D scaled prismatic model of the figure. The 2-D model includes a plurality of links connected by revolute joints to form is a branched, linear-chain of connected links. The registering produces a state trajectory for the figure in the sequence of images. During a reconstructing step, a 3-D model is fitted to the state trajectory to estimate kinematic parameters, and the estimated kinematic parameters are refined using an expectation maximization technique.
摘要翻译：在计算机化方法中，以单目相机测量的2-D图像序列跟踪移动铰接图。使用该图的二维缩放棱镜模型将图像彼此单独地注册。 2-D模型包括通过旋转接头连接以形成的多个连接件，是连接的分支的直链链。记录在图像序列中产生图形的状态轨迹。在重建步骤中，将3-D模型拟合到状态轨迹以估计运动学参数，并且使用期望最大化技术来改进估计的运动学参数。

7. 发明授权

US06240198B1 Method for figure tracking using 2-D registration 失效
标题翻译：使用二维配准的图形跟踪方法
公开(公告)号：US06240198B1
公开(公告)日：2001-05-29
申请号：US09059478
申请日：1998-04-13
申请人： James Matthew Rehg , Daniel D. Morris
发明人： James Matthew Rehg , Daniel D. Morris
IPC分类号： G06K900
CPC分类号： G06K9/00369 , G06T7/246
摘要： In a computerized method, a moving articulated figure is tracked in a sequence of 2-D images measured by a monocular camera. The images are individually registered with each other using a 2-D scaled prismatic model of the figure. The 2-D model includes a plurality of links connected by revolute joints to form is a branched, linear-chain of connected links. The registering produces a state trajectory for the figure in the sequence of images. During a reconstructing step, a 3-D model is fitted to the state trajectory to estimate kinematic parameters, and the estimated kinematic parameters are refined using an expectation maximization technique.
摘要翻译：在计算机化方法中，以单目相机测量的2-D图像序列跟踪移动铰接图。使用该图的二维缩放棱镜模型将图像彼此单独地注册。 2-D模型包括通过旋转接头连接以形成的多个连接件，是连接的分支的直链链。记录在图像序列中产生图形的状态轨迹。在重建步骤中，将3-D模型拟合到状态轨迹以估计运动学参数，并且使用期望最大化技术来改进估计的运动学参数。

8. 发明授权

US06353679B1 Sample refinement method of multiple mode probability density estimation 失效
公开(公告)号：US06353679B1
公开(公告)日：2002-03-05
申请号：US09185278
申请日：1998-11-03
申请人： Tat-Jen Cham , James Matthew Rehg
发明人： Tat-Jen Cham , James Matthew Rehg
IPC分类号： G06K900
CPC分类号： G06K9/6226 , G06T7/277
摘要： The invention recognizes that a probability density function for fitting a model to a complex set of data often has multiple modes, each mode representing a reasonably probable state of the model when compared with the data. Particularly, sequential data such as are collected from detection of moving objects in three dimensional space are placed into data frames. Also, a single frame of data may require analysis by a sequence of analysis operations. Computation of the probability density function of the model state involves two main stages: (1) state prediction, in which the prior probability distribution is generated from information known prior to the availability of the data, and (2) state update, in which the posterior probability distribution is formed by updating the prior distribution with information obtained from observing the data. In particular this information obtained purely from data observations can also be expressed as a probability density function, known as the likelihood function. The likelihood function is a multimodal (multiple peaks) function when a single data frame leads to multiple distinct measurements from which the correct measurement associated with the model cannot be distinguished. The invention analyzes a multimodal likelihood function by numerically searching the likelihood function for peaks. The numerical search proceeds by randomly sampling from the prior distribution to select a number of seed points in state-space, and then numerically finding the maxima of the likelihood function starting from each seed point. Furthermore, kernel functions are fitted to these peaks to represent the likelihood function as an analytic function. The resulting posterior distribution is also multimodal and represented using a set of kernel functions. It is computed by combining the prior distribution and the likelihood function using Bayes Rule. The peaks in the posterior distribution are also referred to as ‘hypotheses’, as they are hypotheses for the states of the model which best explain both the data and the prior knowledge.

9. 发明授权

US06269172B1 Method for tracking the motion of a 3-D figure 失效
标题翻译：跟踪三维图形运动的方法
公开(公告)号：US06269172B1
公开(公告)日：2001-07-31
申请号：US09059651
申请日：1998-04-13
申请人： James Matthew Rehg , Daniel D. Morris
发明人： James Matthew Rehg , Daniel D. Morris
IPC分类号： G06K900
CPC分类号： G06K9/00369 , G06T7/20 , G06T7/246
摘要： In a computerized method, a moving articulated figure is tracked in a sequence of 2-D images measured by a monocular camera. The images are individually registered with each other using a 2-D scaled prismatic model of the figure. The 2-D model includes a plurality of links connected by revolute joints to form is a branched, linear-chain of connected links. The registering produces a state trajectory for the figure in the sequence of images. During a reconstructing step, a 3-D model is fitted to the state trajectory to estimate kinematic parameters, and the estimated kinematic parameters are refined using an expectation maximization technique.
摘要翻译：在计算机化方法中，以单目相机测量的2-D图像序列跟踪移动铰接图。使用该图的二维缩放棱镜模型将图像彼此单独地注册。 2-D模型包括通过旋转接头连接以形成的多个连接件，是连接的分支的直链链。记录在图像序列中产生图形的状态轨迹。在重建步骤中，将3-D模型拟合到状态轨迹以估计运动学参数，并且使用期望最大化技术来改进估计的运动学参数。

10. 发明授权

US06314204B1 Multiple mode probability density estimation with application to multiple hypothesis tracking 有权
公开(公告)号：US06314204B1
公开(公告)日：2001-11-06
申请号：US09185280
申请日：1998-11-03
申请人： Tat-Jen Cham , James Matthew Rehg
发明人： Tat-Jen Cham , James Matthew Rehg
IPC分类号： G06K900
CPC分类号： G06K9/6278 , G06K9/6206 , G06K9/6226 , G06T7/277
摘要： The invention recognizes that a probability density function for fitting a model to a complex set of data often has multiple modes, each mode representing a reasonably probable state of the model when compared with the data. Particularly, sequential data such as are collected from detection of moving objects in three dimensional space are placed into data frames. Computation of the probability density function of the model state involves two main stages: (1) state prediction, in which the prior probability distribution is generated from information known prior to the availability of the data, and (2) state update, in which the posterior probability distribution is formed by updating the prior distribution with information obtained from observing the data. In particular this information obtained purely from data observations can also be expressed as a probability density function, known as the likelihood function. The likelihood function is a multimodal (multiple peaks) function when a single data frame leads to multiple distinct measurements from which the correct measurement associated with the model cannot be distinguished. The invention analyzes a multimodal likelihood function by numerically searching the likelihood function for peaks. The numerical search proceeds by randomly sampling from the prior distribution to select a number of seed points in state-space, and then numerically finding the maxima of the likelihood function starting from each seed point. Furthermore, kernel functions are fitted to these peaks to represent the likelihood function as an analytic function. The resulting posterior distribution is also multimodal and represented using a set of kernel functions. It is computed by combining the prior distribution and the likelihood function using Bayes Rule. The peaks in the posterior distribution are also referred to as ‘hypotheses’, as they are hypotheses for the states of the model which best explain both the data and the prior knowledge.

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