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1. 发明授权

US08270732B2 Clustering nodes in a self-organizing map using an adaptive resonance theory network 有权
标题翻译：使用自适应谐振理论网络的自组织图中的聚类节点
公开(公告)号：US08270732B2
公开(公告)日：2012-09-18
申请号：US12551154
申请日：2009-08-31
申请人： Wesley Kenneth Cobb , David Friedlander , Kishor Adinath Saitwal , Ming-Jung Seow , Gang Xu
发明人： Wesley Kenneth Cobb , David Friedlander , Kishor Adinath Saitwal , Ming-Jung Seow , Gang Xu
IPC分类号： G06K9/62 , G01V3/00
CPC分类号： G06K9/6222
摘要： Techniques are disclosed for discovering object type clusters using pixel-level micro-features extracted from image data. A self-organizing map and adaptive resonance theory (SOM-ART) network is used to classify objects depicted in the image data based on the pixel-level micro-features. Importantly, the discovery of the object type clusters is unsupervised, i.e., performed independent of any training data that defines particular objects, allowing a behavior-recognition system to forgo a training phase and for object classification to proceed without being constrained by specific object definitions. The SOM-ART network is adaptive and able to learn while discovering the object type clusters and classifying objects.
摘要翻译：公开了使用从图像数据提取的像素级微特征来发现对象类型簇的技术。自组织图和自适应共振理论（SOM-ART）网络用于基于像素级微特征对图像数据中描绘的对象进行分类。重要的是，对象类型簇的发现是无监督的，即独立于定义特定对象的任何训练数据执行，允许行为识别系统放弃训练阶段，并且对象分类进行而不受特定对象定义的约束。 SOM-ART网络是自适应的，能够在发现对象类型集群并对对象进行分类时学习。

2. 发明申请

US20120163670A1 BEHAVIORAL RECOGNITION SYSTEM 有权
标题翻译：行为识别系统
公开(公告)号：US20120163670A1
公开(公告)日：2012-06-28
申请号：US13413549
申请日：2012-03-06
申请人： John Eric EATON , Wesley Kenneth COBB , Dennis Gene URECH , Bobby Ernest BLYTHE , David Samuel FRIEDLANDER , Rajkiran Kumar GOTTUMUKKAL , Lon William RISINGER , Kishor Adinath SAITWAL , Ming-Jung SEOW , David Marvin SOLUM , Gang XU , Tao YANG
发明人： John Eric EATON , Wesley Kenneth COBB , Dennis Gene URECH , Bobby Ernest BLYTHE , David Samuel FRIEDLANDER , Rajkiran Kumar GOTTUMUKKAL , Lon William RISINGER , Kishor Adinath SAITWAL , Ming-Jung SEOW , David Marvin SOLUM , Gang XU , Tao YANG
IPC分类号： G06K9/00
CPC分类号： G06K9/00771 , G08B13/19608 , G08B13/19613
摘要： Embodiments of the present invention provide a method and a system for analyzing and learning behavior based on an acquired stream of video frames. Objects depicted in the stream are determined based on an analysis of the video frames. Each object may have a corresponding search model used to track an object's motion frame-to-frame. Classes of the objects are determined and semantic representations of the objects are generated. The semantic representations are used to determine objects' behaviors and to learn about behaviors occurring in an environment depicted by the acquired video streams. This way, the system learns rapidly and in real-time normal and abnormal behaviors for any environment by analyzing movements or activities or absence of such in the environment and identifies and predicts abnormal and suspicious behavior based on what has been learned.
摘要翻译：本发明的实施例提供了一种基于所获取的视频帧流来分析和学习行为的方法和系统。基于对视频帧的分析来确定流中描绘的对象。每个对象可以具有用于跟踪对象的帧到帧的运动的对应的搜索模型。确定对象的类，并生成对象的语义表示。语义表示用于确定对象的行为，并了解在所获取的视频流中描绘的环境中发生的行为。通过这种方式，系统通过分析环境中的动作或活动或不存在这些环境，快速，实时地对任何环境的正常和异常行为进行学习，并根据所学到的知识和预测异常和可疑行为。

3. 发明申请

US20110044492A1 ADAPTIVE VOTING EXPERTS FOR INCREMENTAL SEGMENTATION OF SEQUENCES WITH PREDICTION IN A VIDEO SURVEILLANCE SYSTEM 有权
标题翻译：适应性视频监控系统预测序列分段的自适应投票专家
公开(公告)号：US20110044492A1
公开(公告)日：2011-02-24
申请号：US12543379
申请日：2009-08-18
申请人： WESLEY KENNETH COBB , Bobby Ernest Blythe , David Samuel Friedlander , Kishor Adinath Saitwal , Gang Xu
发明人： WESLEY KENNETH COBB , Bobby Ernest Blythe , David Samuel Friedlander , Kishor Adinath Saitwal , Gang Xu
IPC分类号： G06K9/00
CPC分类号： G06K9/00771
摘要： A sequence layer in a machine-learning engine configured to learn from the observations of a computer vision engine. In one embodiment, the machine-learning engine uses the voting experts to segment adaptive resonance theory (ART) network label sequences for different objects observed in a scene. The sequence layer may be configured to observe the ART label sequences and incrementally build, update, and trim, and reorganize an ngram trie for those label sequences. The sequence layer computes the entropies for the nodes in the ngram trie and determines a sliding window length and vote count parameters. Once determined, the sequence layer may segment newly observed sequences to estimate the primitive events observed in the scene as well as issue alerts for inter-sequence and intra-sequence anomalies.
摘要翻译：机器学习引擎中的序列层，被配置为从计算机视觉引擎的观察中学习。在一个实施例中，机器学习引擎使用投票专家来分割在场景中观察到的不同对象的自适应共振理论（ART）网络标签序列。序列层可以被配置为观察ART标签序列并且逐渐地构建，更新和修整和重组那些标记序列的ngram特里。序列层计算ngram trie中节点的熵，并确定滑动窗口长度和投票计数参数。一旦确定，序列层可以划分新观察到的序列以估计在场景中观察到的原始事件以及发出序列间和序列内异常的警报。

4. 发明申请

US20090087096A1 BACKGROUND-FOREGROUND MODULE FOR VIDEO ANALYSIS SYSTEM 有权
标题翻译：视频分析系统背景模块
公开(公告)号：US20090087096A1
公开(公告)日：2009-04-02
申请号：US12129521
申请日：2008-05-29
申请人： John Eric Eaton , Wesley Kenneth Cobb , Bobby Ernest Blythe , Kishor Adinath Saitwal , Tao Yang , Ming-Jung Seow
发明人： John Eric Eaton , Wesley Kenneth Cobb , Bobby Ernest Blythe , Kishor Adinath Saitwal , Tao Yang , Ming-Jung Seow
IPC分类号： G06K9/46 , G06K9/62
CPC分类号： G06K9/38
摘要： Embodiments of the present invention provide a method and a module for identifying a background of a scene depicted in an acquired stream of video frames that may be used by a video-analysis system. For each pixel or block of pixels in an acquired video frame a comparison measure is determined. The comparison measure depends on difference of color values exhibited in the acquired video frame and in a background image respectively by the pixel or block of pixels and a corresponding pixel and block of pixels in the background image. To determine the comparison measure, the resulting difference is considered in relation to a range of possible color values. If the comparison measure is above a dynamically adjusted threshold, the pixel or the block of pixels is classified as a part of the background of the scene.
摘要翻译：本发明的实施例提供了一种用于识别视频分析系统可以使用的所获取的视频帧流中描绘的场景的背景的方法和模块。对于所获取的视频帧中的每个像素或像素块，确定比较度量。比较测量取决于所获取的视频帧和背景图像中分别由像素或像素块以及背景图像中的对应像素和像素块显示的颜色值的差异。为了确定比较测量，考虑到与可能的颜色值的范围的差异。如果比较度量高于动态调整的阈值，像素或像素块被分类为场景背景的一部分。

5. 发明申请

US20090016600A1 COGNITIVE MODEL FOR A MACHINE-LEARNING ENGINE IN A VIDEO ANALYSIS SYSTEM 有权
标题翻译：视频分析系统中机器学习发动机的认知模型
公开(公告)号：US20090016600A1
公开(公告)日：2009-01-15
申请号：US12170283
申请日：2008-07-09
申请人： JOHN ERIC EATON , Wesley Kenneth Cobb , Dennis G. Urech , David S. Friedlander , Gang Xu , Ming-Jung Seow , Lon W. Risinger , David M. Solum , Tao Yang , Rajkiran K. Gottumukkal , Kishor Adinath Saitwal
发明人： JOHN ERIC EATON , Wesley Kenneth Cobb , Dennis G. Urech , David S. Friedlander , Gang Xu , Ming-Jung Seow , Lon W. Risinger , David M. Solum , Tao Yang , Rajkiran K. Gottumukkal , Kishor Adinath Saitwal
IPC分类号： G06K9/62 , G06K9/00 , G06F15/18
CPC分类号： G06K9/00718 , G06F17/30598 , G06K9/00335 , G06K9/00751 , G06K9/00771 , G06K9/6215 , G06K9/66 , G06K2009/00738 , G06N99/005
摘要： A machine-learning engine is disclosed that is configured to recognize and learn behaviors, as well as to identify and distinguish between normal and abnormal behavior within a scene, by analyzing movements and/or activities (or absence of such) over time. The machine-learning engine may be configured to evaluate a sequence of primitive events and associated kinematic data generated for an object depicted in a sequence of video frames and a related vector representation. The vector representation is generated from a primitive event symbol stream and a phase space symbol stream, and the streams describe actions of the objects depicted in the sequence of video frames.
摘要翻译：公开了一种机器学习引擎，其被配置为通过随时间分析移动和/或活动（或不存在）来识别和学习行为，以及识别和区分场景内的正常和异常行为。机器学习引擎可以被配置为评估为视频帧序列和相关向量表示中描绘的对象生成的原始事件序列和相关联的运动学数据序列。从原始事件符号流和相位空间符号流生成向量表示，并且流描述在视频帧序列中描绘的对象的动作。

6. 发明授权

US08285046B2 Adaptive update of background pixel thresholds using sudden illumination change detection 有权
标题翻译：使用突然照明变化检测自适应更新背景像素阈值
公开(公告)号：US08285046B2
公开(公告)日：2012-10-09
申请号：US12388409
申请日：2009-02-18
申请人： Wesley Kenneth Cobb , Kishor Adinath Saitwal , Bobby Ernest Blythe , Tao Yang
发明人： Wesley Kenneth Cobb , Kishor Adinath Saitwal , Bobby Ernest Blythe , Tao Yang
IPC分类号： G06K9/34
CPC分类号： G06K9/00771
摘要： Techniques are disclosed for a computer vision engine to update both a background model and thresholds used to classify pixels as depicting scene foreground or background in response to detecting that a sudden illumination changes has occurred in a sequence of video frames. The threshold values may be used to specify how much pixel a given pixel may differ from corresponding values in the background model before being classified as depicting foreground. When a sudden illumination change is detected, the values for pixels affected by sudden illumination change may be used to update the value in the background image to reflect the value for that pixel following the sudden illumination change as well as update the threshold for classifying that pixel as depicting foreground/background in subsequent frames of video.
摘要翻译：公开了用于计算机视觉引擎的技术，用于更新背景模型和用于将像素分类为描绘场景前景或背景的阈值，以响应于检测到在视频帧序列中已经发生突然的照明变化。可以使用阈值来指定给定像素在分类为描绘前景之前可能与背景模型中的对应值不同的像素。当检测到突然的照明变化时，可以使用受突然照明改变影响的像素的值来更新背景图像中的值，以反映在突然照射变化之后该像素的值，并且更新用于对该像素进行分类的阈值作为描绘后续视频帧中的前景/背景。

7. 发明授权

US08189905B2 Cognitive model for a machine-learning engine in a video analysis system 有权
标题翻译：视频分析系统中机器学习引擎的认知模型
公开(公告)号：US08189905B2
公开(公告)日：2012-05-29
申请号：US12170283
申请日：2008-07-09
申请人： John Eric Eaton , Wesley Kenneth Cobb , Dennis G. Urech , David S. Friedlander , Gang Xu , Ming-Jung Seow , Lon W. Risinger , David M. Solum , Tao Yang , Rajkiran K. Gottumukkal , Kishor Adinath Saitwal
发明人： John Eric Eaton , Wesley Kenneth Cobb , Dennis G. Urech , David S. Friedlander , Gang Xu , Ming-Jung Seow , Lon W. Risinger , David M. Solum , Tao Yang , Rajkiran K. Gottumukkal , Kishor Adinath Saitwal
IPC分类号： G06K9/62 , G06K9/00
CPC分类号： G06K9/00718 , G06F17/30598 , G06K9/00335 , G06K9/00751 , G06K9/00771 , G06K9/6215 , G06K9/66 , G06K2009/00738 , G06N99/005
摘要： A machine-learning engine is disclosed that is configured to recognize and learn behaviors, as well as to identify and distinguish between normal and abnormal behavior within a scene, by analyzing movements and/or activities (or absence of such) over time. The machine-learning engine may be configured to evaluate a sequence of primitive events and associated kinematic data generated for an object depicted in a sequence of video frames and a related vector representation. The vector representation is generated from a primitive event symbol stream and a phase space symbol stream, and the streams describe actions of the objects depicted in the sequence of video frames.
摘要翻译：公开了一种机器学习引擎，其被配置为通过随时间分析移动和/或活动（或不存在）来识别和学习行为，以及识别和区分场景内的正常和异常行为。机器学习引擎可以被配置为评估为视频帧序列和相关向量表示中描绘的对象生成的原始事件序列和相关联的运动学数据序列。从原始事件符号流和相位空间符号流生成向量表示，并且流描述在视频帧序列中描绘的对象的动作。

8. 发明授权

US08175333B2 Estimator identifier component for behavioral recognition system 有权
标题翻译：用于行为识别系统的估计器标识符组件
公开(公告)号：US08175333B2
公开(公告)日：2012-05-08
申请号：US12208526
申请日：2008-09-11
申请人： John Eric Eaton , Wesley Kenneth Cobb , Rajkiran K. Gottumukkal , Ming-Jung Seow , Tao Yang , Kishor Adinath Saitwal
发明人： John Eric Eaton , Wesley Kenneth Cobb , Rajkiran K. Gottumukkal , Ming-Jung Seow , Tao Yang , Kishor Adinath Saitwal
IPC分类号： G06K9/00
CPC分类号： G06F15/16
摘要： An estimator/identifier component for a computer vision engine of a machine-learning based behavior-recognition system is disclosed. The estimator/identifier component may be configured to classify an object being one of two or more classification types, e.g., as being a vehicle or a person. Once classified, the estimator/identifier may evaluate the object to determine a set of kinematic data, static data, and a current pose of the object. The output of the estimator/identifier component may include the classifications assigned to a tracked object, as well as the derived information and object attributes.
摘要翻译：公开了一种基于机器学习的行为识别系统的计算机视觉引擎的估计/标识符组件。估计器/标识符组件可以被配置为将作为两个或更多个分类类型之一的对象进行分类，例如，作为车辆或人。一旦分类，估计器/标识符可以评估对象以确定一组运动学数据，静态数据和对象的当前姿态。估计器/标识符组件的输出可以包括分配给跟踪对象的分类以及派生的信息和对象属性。

9. 发明授权

US08131012B2 Behavioral recognition system 有权
标题翻译：行为识别系统
公开(公告)号：US08131012B2
公开(公告)日：2012-03-06
申请号：US12028484
申请日：2008-02-08
申请人： John Eric Eaton , Wesley Kenneth Cobb , Dennis Gene Urech , Bobby Ernest Blythe , David Samuel Friedlander , Rajkiran Kumar Gottumukkal , Lon William Risinger , Kishor Adinath Saitwal , Ming-Jung Seow , David Marvin Solum , Gang Xu , Tao Yang
发明人： John Eric Eaton , Wesley Kenneth Cobb , Dennis Gene Urech , Bobby Ernest Blythe , David Samuel Friedlander , Rajkiran Kumar Gottumukkal , Lon William Risinger , Kishor Adinath Saitwal , Ming-Jung Seow , David Marvin Solum , Gang Xu , Tao Yang
IPC分类号： G06K9/00
CPC分类号： G06K9/00771 , G08B13/19608 , G08B13/19613
摘要： Embodiments of the present invention provide a method and a system for analyzing and learning behavior based on an acquired stream of video frames. Objects depicted in the stream are determined based on an analysis of the video frames. Each object may have a corresponding search model used to track an object's motion frame-to-frame. Classes of the objects are determined and semantic representations of the objects are generated. The semantic representations are used to determine objects' behaviors and to learn about behaviors occurring in an environment depicted by the acquired video streams. This way, the system learns rapidly and in real-time normal and abnormal behaviors for any environment by analyzing movements or activities or absence of such in the environment and identifies and predicts abnormal and suspicious behavior based on what has been learned.
摘要翻译：本发明的实施例提供了一种基于所获取的视频帧流来分析和学习行为的方法和系统。基于对视频帧的分析来确定流中描绘的对象。每个对象可以具有用于跟踪对象的帧到帧的运动的对应的搜索模型。确定对象的类，并生成对象的语义表示。语义表示用于确定对象的行为，并了解在所获取的视频流中描绘的环境中发生的行为。通过这种方式，系统通过分析环境中的动作或活动或不存在这些环境，快速，实时地对任何环境的正常和异常行为进行学习，并根据所学到的知识和预测异常和可疑行为。

10. 发明授权

US08094943B2 Background-foreground module for video analysis system 有权
标题翻译：视频分析系统的背景前景模块
公开(公告)号：US08094943B2
公开(公告)日：2012-01-10
申请号：US12129521
申请日：2008-05-29
申请人： John Eric Eaton , Wesley Kenneth Cobb , Bobby Ernest Blythe , Kishor Adinath Saitwal , Tao Yang , Ming-Jung Seow
发明人： John Eric Eaton , Wesley Kenneth Cobb , Bobby Ernest Blythe , Kishor Adinath Saitwal , Tao Yang , Ming-Jung Seow
IPC分类号： G06K9/46 , G06K9/62 , G06K9/00
CPC分类号： G06K9/38
摘要： Embodiments of the present invention provide a method and a module for identifying a background of a scene depicted in an acquired stream of video frames that may be used by a video-analysis system. For each pixel or block of pixels in an acquired video frame a comparison measure is determined. The comparison measure depends on difference of color values exhibited in the acquired video frame and in a background image respectively by the pixel or block of pixels and a corresponding pixel and block of pixels in the background image. To determine the comparison measure, the resulting difference is considered in relation to a range of possible color values. If the comparison measure is above a dynamically adjusted threshold, the pixel or the block of pixels is classified as a part of the background of the scene.
摘要翻译：本发明的实施例提供了一种用于识别视频分析系统可以使用的所获取的视频帧流中描绘的场景的背景的方法和模块。对于所获取的视频帧中的每个像素或像素块，确定比较度量。比较测量取决于所获取的视频帧和背景图像中分别由像素或像素块以及背景图像中的对应像素和像素块显示的颜色值的差异。为了确定比较测量，考虑到与可能的颜色值的范围的差异。如果比较度量高于动态调整的阈值，像素或像素块被分类为场景背景的一部分。

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