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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
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。
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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。
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3.发明授权公开(公告)号：US08180105B2
公开(公告)日：2012-05-15
申请号：US12561956
申请日：2009-09-17
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。
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4.发明授权Video surveillance system configured to analyze complex behaviors using alternating layers of clustering and sequencing 有权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
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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5.发明授权Detecting anomalous events using a long-term memory in a video analysis system 有权在视频分析系统中使用长期记忆来检测异常事件公开(公告)号：US08126833B2
公开(公告)日：2012-02-28
申请号：US12336354
申请日：2008-12-16
申请人： Wesley Kenneth Cobb , Ming-Jung Seow , Gang Xu
发明人： Wesley Kenneth Cobb , Ming-Jung Seow , Gang Xu
CPC分类号： G06K9/00771 , G06K9/6215
摘要： Techniques are described for detecting anomalous events using a long-term memory in a video analysis system. The long-term memory may be used to store and retrieve information learned while a video analysis system observes a stream of video frames depicting a given scene. Further, the long-term memory may be configured to detect the occurrence of anomalous events, relative to observations of other events that have occurred in the scene over time. A distance measure may used to determine a distance between an active percept (encoding an observed event depicted in the stream of video frames) and a retrieved percept (encoding a memory of previously observed events in the long-term memory). If the distance exceeds a specified threshold, the long-term memory may publish the occurrence of an anomalous event for review by users of the system.
摘要翻译： 描述了用于在视频分析系统中使用长期存储器来检测异常事件的技术。 当视频分析系统观察描绘给定场景的视频帧流时，长期记忆可用于存储和检索学习的信息。 此外，长期记忆可以被配置为相对于在时间上在场景中发生的其他事件的观察来检测异常事件的发生。 距离度量可以用于确定活动感知（编码在视频帧流中描绘的观察到的事件）与检索到的感知（编码长期存储器中先前观察到的事件的存储器）之间的距离。 如果距离超过指定的阈值，长期记忆可能会发布异常事件的发生，供系统用户审阅。
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6.发明授权公开(公告)号：US08189905B2
公开(公告)日：2012-05-29
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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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。
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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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公开(公告)号：US09633275B2
公开(公告)日：2017-04-25
申请号：US12543141
申请日：2009-08-18
申请人： Wesley Kenneth Cobb , Rajkiran K. Gottumukkal , Kishor Adinath Saitwal , Ming-Jung Seow , Gang Xu , Lon W. Risinger , Jeff Graham
发明人： Wesley Kenneth Cobb , Rajkiran K. Gottumukkal , Kishor Adinath Saitwal , Ming-Jung Seow , Gang Xu , Lon W. Risinger , Jeff Graham
CPC分类号： G06K9/46 , G06K9/3233 , G06K9/4647 , G06K9/48 , G06K2009/485 , H04N7/18
摘要： Techniques are disclosed for extracting micro-features at a pixel-level based on characteristics of one or more images. Importantly, the extraction is unsupervised, i.e., performed independent of any training data that defines particular objects, allowing a behavior-recognition system to forgo a training phase and for object classification to proceed without being constrained by specific object definitions. A micro-feature extractor that does not require training data is adaptive and self-trains while performing the extraction. The extracted micro-features are represented as a micro-feature vector that may be input to a micro-classifier which groups objects into object type clusters based on the micro-feature vectors.
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10.发明授权公开(公告)号：US08494222B2
公开(公告)日：2013-07-23
申请号：US13472214
申请日：2012-05-15
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。
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