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。

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。

公开(公告)号：US08797405B2

公开(公告)日：2014-08-05

申请号：US12551332

申请日：2009-08-31

申请人： Wesley Kenneth Cobb ,  Bobby Ernest Blythe ,  David Samuel Friedlander ,  Rajkiran Kumar Gottumukkal ,  Kishor Adinath Saitwal ,  Ming-Jung Seow ,  Gang Xu

发明人： Wesley Kenneth Cobb ,  Bobby Ernest Blythe ,  David Samuel Friedlander ,  Rajkiran Kumar Gottumukkal ,  Kishor Adinath Saitwal ,  Ming-Jung Seow ,  Gang Xu

IPC分类号： H04N7/18 ,  G06K9/00

CPC分类号： G06K9/00771 ,  G06K9/00973

摘要： Techniques are disclosed for visually conveying classifications derived from pixel-level micro-features extracted from image data. The image data may include an input stream of video frames depicting one or more foreground objects. The classifications represent information learned by a video surveillance system. A request may be received to view a classification. A visual representation of the classification may be generated. A user interface may be configured to display the visual representation of the classification and to allow a user to view and/or modify properties associated with the classification.

摘要翻译： 公开了用于视觉地传送从图像数据提取的像素级微特征得出的分类的技术。 图像数据可以包括描绘一个或多个前景对象的视频帧的输入流。 分类表示视频监控系统学到的信息。 可以接收到查看分类的请求。 可以生成分类的视觉表示。 用户界面可以被配置为显示分类的可视表示，并允许用户查看和/或修改与分类相关联的属性。

公开(公告)号：US08620028B2

公开(公告)日：2013-12-31

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： G06K9/62

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。

公开(公告)号：US08493409B2

公开(公告)日：2013-07-23

申请号：US12543351

申请日：2009-08-18

申请人： Wesley Kenneth Cobb ,  Bobby Ernest Blythe ,  David Samuel Friedlander ,  Rajkiran Kumar Gottumukkal ,  Kishor Adinath Saitwal

发明人： Wesley Kenneth Cobb ,  Bobby Ernest Blythe ,  David Samuel Friedlander ,  Rajkiran Kumar Gottumukkal ,  Kishor Adinath Saitwal

IPC分类号： G09G5/14 ,  G06T17/05 ,  G06T15/04 ,  G06T11/00 ,  G06T15/08

CPC分类号： G06T17/05 ,  G06K9/00771 ,  G06K9/6251 ,  G06T7/194 ,  G06T7/215 ,  G06T11/00 ,  G06T11/206 ,  G06T15/04 ,  G06T15/08 ,  G06T2207/20084 ,  H04N5/272

摘要： Techniques are disclosed for visually conveying a sequence storing an ordered string of symbols generated from kinematic data derived from analyzing an input stream of video frames depicting one or more foreground objects. The sequence may represent information learned by a video surveillance system. A request may be received to view the sequence or a segment partitioned form the sequence. A visual representation of the segment may be generated and superimposed over a background image associated with the scene. A user interface may be configured to display the visual representation of the sequence or segment and to allow a user to view and/or modify properties associated with the sequence or segment.

摘要翻译： 公开了用于视觉地传达存储从分析描绘一个或多个前景对象的视频帧的输入流导出的运动学数据产生的符号的有序串的序列的技术。 序列可以表示由视频监控系统学习的信息。 可以接收请求以查看序列或从序列分割的段。 可以生成片段的视觉表示并叠加在与场景相关联的背景图像上。 用户界面可以被配置为显示序列或段的可视表示，并且允许用户查看和/或修改与序列或段相关联的属性。

公开(公告)号：US08340352B2

公开(公告)日：2012-12-25

申请号：US12543318

申请日：2009-08-18

申请人： Wesley Kenneth Cobb ,  David Samuel Friedlander ,  Kishor Adinath Saitwal

发明人： Wesley Kenneth Cobb ,  David Samuel Friedlander ,  Kishor Adinath Saitwal

IPC分类号： G06K9/00

CPC分类号： G06K9/00771 ,  G06K9/6222 ,  G06K9/6296 ,  G06T7/248 ,  G06T2207/10016 ,  G06T2207/30196 ,  G06T2207/30232 ,  G06T2207/30236 ,  G06T2207/30241

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。

公开(公告)号：US08295591B2

公开(公告)日：2012-10-23

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。

公开(公告)号：US20110043536A1

公开(公告)日：2011-02-24

申请号：US12543351

申请日：2009-08-18

申请人： WESLEY KENNETH COBB ,  Bobby Ernest Blythe ,  David Samuel Friedlander ,  Rajkiran Kumar Gottumukkal ,  Kishor Adinath Saitwal

发明人： WESLEY KENNETH COBB ,  Bobby Ernest Blythe ,  David Samuel Friedlander ,  Rajkiran Kumar Gottumukkal ,  Kishor Adinath Saitwal

IPC分类号： G09G5/00

CPC分类号： G06T17/05 ,  G06K9/00771 ,  G06K9/6251 ,  G06T7/194 ,  G06T7/215 ,  G06T11/00 ,  G06T11/206 ,  G06T15/04 ,  G06T15/08 ,  G06T2207/20084 ,  H04N5/272

摘要： Techniques are disclosed for visually conveying a sequence storing an ordered string of symbols generated from kinematic data derived from analyzing an input stream of video frames depicting one or more foreground objects. The sequence may represent information learned by a video surveillance system. A request may be received to view the sequence or a segment partitioned form the sequence. A visual representation of the segment may be generated and superimposed over a background image associated with the scene. A user interface may be configured to display the visual representation of the sequence or segment and to allow a user to view and/or modify properties associated with the sequence or segment.

摘要翻译： 公开了用于视觉地传达存储从分析描绘一个或多个前景对象的视频帧的输入流导出的运动学数据产生的符号的有序串的序列的技术。 序列可以表示由视频监控系统学习的信息。 可以接收请求以查看序列或从序列分割的段。 可以生成片段的视觉表示并叠加在与场景相关联的背景图像上。 用户界面可以被配置为显示序列或段的可视表示，并且允许用户查看和/或修改与序列或段相关联的属性。

公开(公告)号：US08379085B2

公开(公告)日：2013-02-19

申请号：US12543307

申请日：2009-08-18

申请人： Wesley Kenneth Cobb ,  David Samuel Friedlander ,  Kishor Adinath Saitwal

发明人： Wesley Kenneth Cobb ,  David Samuel Friedlander ,  Kishor Adinath Saitwal

IPC分类号： H04N7/18 ,  H04N5/225 ,  G06K9/00 ,  G06K9/62

CPC分类号： G06K9/00771 ,  G06K9/6251 ,  G06K9/726

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。

公开(公告)号：US20110050897A1

公开(公告)日：2011-03-03

申请号：US12551332

申请日：2009-08-31

申请人： Wesley Kenneth Cobb ,  Bobby Ernest Blythe ,  David Samuel Friedlander ,  Rajkiran Kumar Gottumukkal ,  Kishor Adinath Saitwal ,  Ming-Jung Seow ,  Gang Xu

发明人： Wesley Kenneth Cobb ,  Bobby Ernest Blythe ,  David Samuel Friedlander ,  Rajkiran Kumar Gottumukkal ,  Kishor Adinath Saitwal ,  Ming-Jung Seow ,  Gang Xu

IPC分类号： H04N7/16

CPC分类号： G06K9/00771 ,  G06K9/00973

摘要： Techniques are disclosed for visually conveying classifications derived from pixel-level micro-features extracted from image data. The image data may include an input stream of video frames depicting one or more foreground objects. The classifications represent information learned by a video surveillance system. A request may be received to view a classification. A visual representation of the classification may be generated. A user interface may be configured to display the visual representation of the classification and to allow a user to view and/or modify properties associated with the classification.

摘要翻译： 公开了用于视觉地传送从图像数据提取的像素级微特征得出的分类的技术。 图像数据可以包括描绘一个或多个前景对象的视频帧的输入流。 分类表示视频监控系统学到的信息。 可能会收到请求以查看分类。 可以生成分类的视觉表示。 用户界面可以被配置为显示分类的可视表示，并允许用户查看和/或修改与分类相关联的属性。