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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。
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2.发明申请公开(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
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。
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3.发明申请SEMANTIC REPRESENTATION MODULE OF A MACHINE-LEARNING ENGINE IN A VIDEO ANALYSIS SYSTEM 有权视频分析系统中机器学习发动机的语义表示模块公开(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。
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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。
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公开(公告)号：US20090087024A1
公开(公告)日：2009-04-02
申请号：US12112864
申请日：2008-04-30
申请人： JOHN ERIC EATON , WESLEY KENNETH COBB , BOBBY ERNEST BLYTHE , RAJKIRAN KUMAR GOTTUMUKKAL , KISHOR ADINATH SAITWAL
发明人： JOHN ERIC EATON , WESLEY KENNETH COBB , BOBBY ERNEST BLYTHE , RAJKIRAN KUMAR GOTTUMUKKAL , KISHOR ADINATH SAITWAL
CPC分类号： G06K9/00771 , G06K9/00335 , G06T7/11 , G06T2207/10016
摘要： Embodiments of the present invention provide a method and a system for mapping a scene depicted in an acquired stream of video frames that may be used by a machine-learning behavior-recognition system. A background image of the scene is segmented into plurality of regions representing various objects of the background image. Statistically similar regions may be merged and associated. The regions are analyzed to determine their z-depth order in relation to a video capturing device providing the stream of the video frames and other regions, using occlusions between the regions and data about foreground objects in the scene. An annotated map describing the identified regions and their properties is created and updated.
摘要翻译： 本发明的实施例提供了一种用于映射可以由机器学习行为识别系统使用的所获取的视频帧流中描绘的场景的方法和系统。 场景的背景图像被分割成表示背景图像的各种对象的多个区域。 统计上相似的地区可能会合并并相关联。 分析这些区域以使用提供视频帧和其他区域的流的视频捕获设备来确定它们的z深度顺序，使用区域之间的遮挡以及关于场景中的前景物体的数据。 创建并更新描述已识别区域及其属性的注释地图。
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公开(公告)号：US20100063949A1
公开(公告)日：2010-03-11
申请号：US12208551
申请日：2008-09-11
CPC分类号： G06N99/005 , G06K9/00335 , G06K9/00771 , G06K9/6296
摘要： A long-term memory used to store and retrieve information learned while a video analysis system observes a stream of video frames is disclosed. The long-term memory provides a memory with a capacity that grows in size gracefully, as events are observed over time. Additionally, the long-term memory may encode events, represented by sub-graphs of a neural network. Further, rather than predefining a number of patterns recognized and manipulated by the long-term memory, embodiments of the invention provide a long-term memory where the size of a feature dimension (used to determine the similarity between different observed events) may grow dynamically as necessary, depending on the actual events observed in a sequence of video frames.
摘要翻译： 公开了一种用于存储和检索在视频分析系统观察视频帧流时学习的信息的长期存储器。 长期记忆提供了一个能力随着时间的推移而随着时间的推移而增长的记忆。 另外，长期记忆可以编码由神经网络的子图表示的事件。 此外，本发明的实施例不是预先确定由长期记忆识别和操纵的多个模式，而是提供长期存储器，其中特征维度（用于确定不同观察到的事件之间的相似性）的大小可以动态地增长 根据需要，取决于视频帧序列中观察到的实际事件。
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