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1. 发明授权

US08797405B2 Visualizing and updating classifications in a video surveillance system 有权
标题翻译：可视化和更新视频监控系统中的分类
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。

2. 发明授权

US08705861B2 Context processor for video analysis system 有权
标题翻译：视频分析系统的上下文处理器
公开(公告)号：US08705861B2
公开(公告)日：2014-04-22
申请号：US13494605
申请日：2012-06-12
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
IPC分类号： G06K9/00 , G06K9/34
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。

3. 发明授权

US08620028B2 Behavioral recognition system 有权
标题翻译：行为识别系统
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。

4. 发明授权

US08493409B2 Visualizing and updating sequences and segments in a video surveillance system 有权
标题翻译：在视频监控系统中可视化和更新序列和片段
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。

5. 发明授权

US08374393B2 Foreground object tracking 有权
标题翻译：前景对象跟踪
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。

6. 发明授权

US08340352B2 Inter-trajectory anomaly detection using adaptive voting experts in a video surveillance system 有权
标题翻译：视频监控系统中使用自适应投票专家的轨迹间异常检测
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。

7. 发明授权

US08180105B2 Classifier anomalies for observed behaviors in a video surveillance system 有权
标题翻译：视频监控系统观察行为的分类器异常
公开(公告)号：US08180105B2
公开(公告)日：2012-05-15
申请号：US12561956
申请日：2009-09-17
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： G06K9/00 , G08G5/00
CPC分类号： G06K9/00771
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 combination of a self organizing map (SOM) and an adaptive resonance theory (ART) network may be used to identify a variety of different anomalous inputs at each cluster layer. As progressively higher layers of the cortex model component represent progressively higher levels of abstraction, anomalies occurring in the higher levels of the cortex model represent observations of behavioral anomalies corresponding to progressively complex patterns of behavior.
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。可以使用自组织图（SOM）和自适应共振理论（ART）网络的组合来识别每个簇层处的各种不同的异常输入。随着逐渐更高层的皮层模型组件代表逐渐提高的抽象水平，在较高级别的皮质模型中发生的异常表示对应于逐渐复杂的行为模式的行为异常的观察。

8. 发明授权

US08170283B2 Video surveillance system configured to analyze complex behaviors using alternating layers of clustering and sequencing 有权
标题翻译：视频监控系统配置为使用交替的聚类和排序层分析复杂行为
公开(公告)号：US08170283B2
公开(公告)日：2012-05-01
申请号：US12561977
申请日：2009-09-17
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： G06K9/00 , G08G5/00
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。

9. 发明申请

US20110064268A1 VIDEO SURVEILLANCE SYSTEM CONFIGURED TO ANALYZE COMPLEX BEHAVIORS USING ALTERNATING LAYERS OF CLUSTERING AND SEQUENCING 有权
标题翻译：视频监控系统配置分析复杂行为使用组合和序列的替代层
公开(公告)号：US20110064268A1
公开(公告)日：2011-03-17
申请号：US12561977
申请日：2009-09-17
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： G06T7/00 , G06T7/20
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。

10. 发明申请

US20110044533A1 VISUALIZING AND UPDATING LEARNED EVENT MAPS IN SURVEILLANCE SYSTEMS 有权
标题翻译：监测和更新监测系统中的有用的事件
公开(公告)号：US20110044533A1
公开(公告)日：2011-02-24
申请号：US12543204
申请日：2009-08-18
申请人： WESLEY KENNETH COBB , BOBBY ERNEST BLYTHE , RAJKIRAN KUMAR GOTTUMUKKAL , MING-JUNG SEOW
发明人： WESLEY KENNETH COBB , BOBBY ERNEST BLYTHE , RAJKIRAN KUMAR GOTTUMUKKAL , MING-JUNG SEOW
IPC分类号： G06K9/62 , H04N7/18
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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