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：US08374393B2

公开(公告)日：2013-02-12

申请号：US13545950

申请日：2012-07-10

申请人： Wesley Kenneth Cobb ,  Ming-Jung Seow ,  Tao Yang

发明人： Wesley Kenneth Cobb ,  Ming-Jung Seow ,  Tao Yang

IPC分类号： G06K9/00

CPC分类号： G06K9/00771 ,  G06T7/215 ,  G06T7/254 ,  G06T2207/10016 ,  G06T2207/30196 ,  G06T2207/30236 ,  G06T2207/30241 ,  G08B13/19608

摘要： Techniques are disclosed for detecting foreground objects in a scene captured by a surveillance system and tracking the detected foreground objects from frame to frame in real time. A motion flow field is used to validate foreground objects(s) that are extracted from the background model of a scene. Spurious foreground objects are filtered before the foreground objects are provided to the tracking stage. The motion flow field is also used by the tracking stage to improve the performance of the tracking as needed for real time surveillance applications.

摘要翻译： 公开了用于检测由监视系统捕获的场景中的前景物体并且实时地从帧到帧跟踪检测到的前景物体的技术。 运动流场用于验证从场景的背景模型中提取的前景对象。 在将前景对象提供给跟踪阶段之前，会对前景对象进行过滤。 跟踪阶段还使用运动流场来提高实时监控应用所需的跟踪性能。

公开(公告)号：US20090087085A1

公开(公告)日：2009-04-02

申请号：US12208538

申请日：2008-09-11

申请人： JOHN ERIC EATON ,  Wesley Kenneth Cobb ,  Rajkiran K. Gottumukkal ,  Kishore Adinath Saitwal ,  Ming-Jung Seow ,  Tao Yang ,  Bobby Ernest Blythe

发明人： JOHN ERIC EATON ,  Wesley Kenneth Cobb ,  Rajkiran K. Gottumukkal ,  Kishore Adinath Saitwal ,  Ming-Jung Seow ,  Tao Yang ,  Bobby Ernest Blythe

IPC分类号： G06F15/18

CPC分类号： G06F15/16

摘要： A tracker component for a computer vision engine of a machine-learning based behavior-recognition system is disclosed. The behavior-recognition system may be configured to learn, identify, and recognize patterns of behavior by observing a video stream (i.e., a sequence of individual video frames). The tracker component may be configured to track objects depicted in the sequence of video frames and to generate, search, match, and update computational models of such objects.

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跟踪器组件。 行为识别系统可以被配置为通过观察视频流（即，各个视频帧的序列）来学习，识别和识别行为模式。 跟踪器组件可以被配置为跟踪视频帧序列中描绘的对象并且生成，搜索，匹配和更新这些对象的计算模型。

公开(公告)号：US08300924B2

公开(公告)日：2012-10-30

申请号：US12208538

申请日：2008-09-11

申请人： John Eric Eaton ,  Wesley Kenneth Cobb ,  Rajkiran K. Gottumukkal ,  Kishor Adinath Saitwal ,  Ming-Jung Seow ,  Tao Yang ,  Bobby Ernest Blythe

发明人： John Eric Eaton ,  Wesley Kenneth Cobb ,  Rajkiran K. Gottumukkal ,  Kishor Adinath Saitwal ,  Ming-Jung Seow ,  Tao Yang ,  Bobby Ernest Blythe

IPC分类号： G06K9/62

CPC分类号： G06F15/16

摘要： A tracker component for a computer vision engine of a machine-learning based behavior-recognition system is disclosed. The behavior-recognition system may be configured to learn, identify, and recognize patterns of behavior by observing a video stream (i.e., a sequence of individual video frames). The tracker component may be configured to track objects depicted in the sequence of video frames and to generate, search, match, and update computational models of such objects.

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跟踪器组件。 行为识别系统可以被配置为通过观察视频流（即，各个视频帧的序列）来学习，识别和识别行为模式。 跟踪器组件可以被配置为跟踪视频帧序列中描绘的对象并且生成，搜索，匹配和更新这些对象的计算模型。

公开(公告)号：US08218818B2

公开(公告)日：2012-07-10

申请号：US12552197

申请日：2009-09-01

申请人： Wesley Kenneth Cobb ,  Ming-Jung Seow ,  Tao Yang

发明人： Wesley Kenneth Cobb ,  Ming-Jung Seow ,  Tao Yang

IPC分类号： G06K9/00

CPC分类号： G06K9/00771 ,  G06T7/215 ,  G06T7/254 ,  G06T2207/10016 ,  G06T2207/30196 ,  G06T2207/30236 ,  G06T2207/30241 ,  G08B13/19608

摘要： Techniques are disclosed for detecting foreground objects in a scene captured by a surveillance system and tracking the detected foreground objects from frame to frame in real time. A motion flow field is used to validate foreground objects(s) that are extracted from the background model of a scene. Spurious foreground objects are filtered before the foreground objects are provided to the tracking stage. The motion flow field is also used by the tracking stage to improve the performance of the tracking as needed for real time surveillance applications.

摘要翻译： 公开了用于检测由监视系统捕获的场景中的前景物体并且实时地从帧到帧跟踪检测到的前景物体的技术。 运动流场用于验证从场景的背景模型中提取的前景对象。 在将前景对象提供给跟踪阶段之前，将对前景对象进行过滤。 跟踪阶段还使用运动流场来提高实时监控应用所需的跟踪性能。

公开(公告)号：US20090087027A1

公开(公告)日：2009-04-02

申请号：US12208526

申请日：2008-09-11

申请人： John Eric Eaton ,  Wesley Kenneth Cobb ,  Rajkiran Kumar Gottumukkal ,  Ming-Jung Seow ,  Tao Yang ,  Kishor Adinath Saitwal

发明人： John Eric Eaton ,  Wesley Kenneth Cobb ,  Rajkiran Kumar Gottumukkal ,  Ming-Jung Seow ,  Tao Yang ,  Kishor Adinath Saitwal

IPC分类号： G06F15/18

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。

公开(公告)号：US08411935B2

公开(公告)日：2013-04-02

申请号：US12170268

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

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。

公开(公告)号：US08358834B2

公开(公告)日：2013-01-22

申请号：US12543336

申请日：2009-08-18

申请人： Wesley Kenneth Cobb ,  Ming-Jung Seow ,  Tao Yang

发明人： Wesley Kenneth Cobb ,  Ming-Jung Seow ,  Tao Yang

IPC分类号： G06K9/00

CPC分类号： G06T7/20 ,  G06K9/00335 ,  G06K9/00718 ,  G06K9/00771 ,  G06K9/6218 ,  G06K9/6222 ,  G06K9/66 ,  G06K2009/00738 ,  G06T7/254 ,  G06T2207/10016 ,  G06T2207/20084 ,  G06T2207/30196 ,  G06T2207/30232

摘要： Techniques are disclosed for learning and modeling a background for a complex and/or dynamic scene over a period of observations without supervision. A background/foreground component of a computer vision engine may be configured to model a scene using an array of ART networks. The ART networks learn the regularity and periodicity of the scene by observing the scene over a period of time. Thus, the ART networks allow the computer vision engine to model complex and dynamic scene backgrounds in video.

摘要翻译： 披露了一种技术，用于在无监督的一段时间内为复杂和/或动态场景学习和建模背景。 计算机视觉引擎的背景/前景分量可以被配置为使用ART网络阵列对场景建模。 ART网络通过观察一段时间内的场景，了解场景的规律性和周期性。 因此，ART网络允许计算机视觉引擎在视频中对复杂和动态的场景背景进行建模。

公开(公告)号：US20090016599A1

公开(公告)日：2009-01-15

申请号：US12170268

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

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。

公开(公告)号：US20080193010A1

公开(公告)日：2008-08-14

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： G06F15/18

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。