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1.发明申请VISUALIZING AND UPDATING SEQUENCES AND SEGMENTS IN A VIDEO SURVEILLANCE SYSTEM 有权视频监控系统中的可视化和更新序列和部分公开(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。
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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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3.发明申请公开(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。
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4.发明申请ADAPTIVE UPDATE OF BACKGROUND PIXEL THRESHOLDS USING SUDDEN ILLUMINATION CHANGE DETECTION 有权使用SUDDEN ILLUMINATION CHANGE DETECTION的背景像素阈值的自适应更新公开(公告)号：US20100208986A1
公开(公告)日：2010-08-19
申请号：US12388409
申请日：2009-02-18
IPC分类号： G06K9/62
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。
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公开(公告)号：US20110044533A1
公开(公告)日：2011-02-24
申请号：US12543204
申请日：2009-08-18
CPC分类号： G06K9/00771 , G08B13/19613
摘要： Techniques are disclosed for visually conveying an event map. The event map may represent information learned by a surveillance system. A request may be received to view the event map for a specified scene. The event map may be generated, including a background model of the specified scene and at least one cluster providing a statistical distribution of an event in the specified scene. Each statistical distribution may be derived from data streams generated from a sequence of video frames depicting the specified scene captured by a video camera. Each event may be observed to occur at a location in the specified scene corresponding to a location of the respective cluster in the event map. The event map may be configured to allow a user to view and/or modify properties associated with each cluster. For example, the user may label a cluster and set events matching the cluster to always (or never) generate an alert.
摘要翻译： 公开了用于视觉传达事件图的技术。 事件地图可以表示监视系统学到的信息。 可以接收到查看指定场景的事件映射的请求。 可以生成事件映射，包括指定场景的背景模型和至少一个提供指定场景中的事件的统计分布的群集。 每个统计分布可以从从描述由摄像机捕获的指定场景的视频帧序列产生的数据流中导出。 可以观察每个事件发生在与事件映射中相应簇的位置相对应的指定场景中的位置处。 可以将事件映射配置为允许用户查看和/或修改与每个集群相关联的属性。 例如，用户可以标记集群并将与集群匹配的事件设置为始终（或从不）生成警报。
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6.发明申请VISUALIZING AND UPDATING LONG-TERM MEMORY PERCEPTS IN A VIDEO SURVEILLANCE SYSTEM 有权在视频监控系统中实现和更新长期记忆公开(公告)号：US20110050896A1
公开(公告)日：2011-03-03
申请号：US12551303
申请日：2009-08-31
CPC分类号： G06K9/6217 , G06K9/00771 , G06K9/00973 , H04N7/18 , H04N7/183
摘要： Techniques are disclosed for visually conveying a percept. The percept may represent information learned by a video surveillance system. A request may be received to view a percept for a specified scene. The percept may have been derived from data streams generated from a sequence of video frames depicting the specified scene captured by a video camera. A visual representation of the percept may be generated. A user interface may be configured to display the visual representation of the percept and to allow a user to view and/or modify metadata attributes with the percept. For example, the user may label a percept and set events matching the percept to always (or never) result in alert being generated for users of the video surveillance system.
摘要翻译： 公开了用于视觉地传达感知的技术。 感知可以表示视频监控系统学到的信息。 可以接收请求以查看指定场景的感知。 感知可能是从描绘由摄像机捕获的指定场景的视频帧序列产生的数据流导出的。 可以产生视觉的视觉表示。 用户界面可以被配置为显示感知的视觉表示，并允许用户使用感知来查看和/或修改元数据属性。 例如，用户可以标记感知并将匹配感知的事件设置为始终（或从不）导致为视频监控系统的用户生成警报。
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7.发明申请INTER-TRAJECTORY ANOMALY DETECTION USING ADAPTIVE VOTING EXPERTS IN A VIDEO SURVEILLANCE SYSTEM 有权在视频监控系统中使用自适应投票专家进行异地检测公开(公告)号：US20110044499A1
公开(公告)日：2011-02-24
申请号：US12543318
申请日：2009-08-18
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。
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8.发明申请INTRA-TRAJECTORY ANOMALY DETECTION USING ADAPTIVE VOTING EXPERTS IN A VIDEO SURVEILLANCE SYSTEM 有权在视频监控系统中使用自适应投票专家进行异常检测公开(公告)号：US20110043626A1
公开(公告)日：2011-02-24
申请号：US12543307
申请日：2009-08-18
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。
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9.发明申请公开(公告)号：US20110044498A1
公开(公告)日：2011-02-24
申请号：US12543242
申请日：2009-08-18
申请人： WESLEY KENNETH COBB , BOBBY ERNEST BLYTHE , DAVID SAMUEL FRIEDLANDER , RAJKRAN KUMAR GOTTUMUKKAL , MING-JENG SEOW , GANG XU
发明人： WESLEY KENNETH COBB , BOBBY ERNEST BLYTHE , DAVID SAMUEL FRIEDLANDER , RAJKRAN KUMAR GOTTUMUKKAL , MING-JENG SEOW , GANG XU
IPC分类号： G06K9/00
CPC分类号： G06T11/206
摘要： Techniques are disclosed for visually conveying a trajectory map. The trajectory map provides users with a visualization of data observed by a machine-learning engine of a behavior recognition system. Further, the visualization may provide an interface used to guide system behavior. For example, the interface may be used to specify that the behavior recognition system should alert (or not alert) when a particular trajectory is observed to occur.
摘要翻译： 公开了用于视觉地传送轨迹图的技术。 轨迹图为用户提供行为识别系统的机器学习引擎所观察到的数据可视化。 此外，可视化可以提供用于指导系统行为的接口。 例如，接口可以用于指定当观察到特定轨迹发生时行为识别系统应该警告（或不警报）。
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10.发明申请公开(公告)号：US20110064268A1
公开(公告)日：2011-03-17
申请号：US12561977
申请日：2009-09-17
CPC分类号： G06K9/6222 , G06K9/00335 , G06K9/00771 , G06K9/6284
摘要： Techniques are disclosed for a video surveillance system to learn to recognize complex behaviors by analyzing pixel data using alternating layers of clustering and sequencing. A video surveillance system may be configured to observe a scene (as depicted in a sequence of video frames) and, over time, develop hierarchies of concepts including classes of objects, actions and behaviors. That is, the video surveillance system may develop models at progressively more complex levels of abstraction used to identify what events and behaviors are common and which are unusual. When the models have matured, the video surveillance system issues alerts on unusual events.
摘要翻译： 公开了用于视频监控系统的技术，以通过使用交替的聚类和排序层分析像素数据来学习识别复杂行为。 视频监视系统可以被配置为观察场景（如视频帧序列所示），并且随着时间的推移，开发包括对象，动作和行为类别的概念的层级。 也就是说，视频监控系统可以开发逐渐更复杂的抽象级别的模型，用于识别哪些事件和行为是常见的，哪些是不寻常的。 当模型成熟时，视频监控系统会发出异常事件警报。
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