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1. 发明授权

US09633275B2 Pixel-level based micro-feature extraction 有权
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
IPC分类号： G06K9/00 , G06K9/62 , G06K9/68 , G06F17/30 , G06F7/00 , G06K9/46
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.

2. 发明授权

US08494222B2 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：US08494222B2
公开(公告)日：2013-07-23
申请号：US13472214
申请日：2012-05-15
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。

3. 发明授权

US08416296B2 Mapper component for multiple art networks in a video analysis system 有权
标题翻译：视频分析系统中多个艺术网络的映射器组件
公开(公告)号：US08416296B2
公开(公告)日：2013-04-09
申请号：US12423650
申请日：2009-04-14
申请人： Wesley Kenneth Cobb , Ming-Jung Seow
发明人： Wesley Kenneth Cobb , Ming-Jung Seow
IPC分类号： H04N7/18
CPC分类号： G06K9/00771 , G06K9/00785
摘要： Techniques are disclosed for detecting the occurrence of unusual events in a sequence of video frames Importantly, what is determined as unusual need not be defined in advance, but can be determined over time by observing a stream of primitive events and a stream of context events. A mapper component may be configured to parse the event streams and supply input data sets to multiple adaptive resonance theory (ART) networks. Each individual ART network may generate clusters from the set of inputs data supplied to that ART network. Each cluster represents an observed statistical distribution of a particular thing or event being observed that ART network.
摘要翻译：公开了用于检测视频帧序列中的异常事件的发生的技术。重要的是，不需要预先定义什么被确定，而是可以通过观察原始事件流和上下文事件流来随时间确定。映射器组件可以被配置为解析事件流并将输入数据集提供给多个自适应谐振理论（ART）网络。每个单独的ART网络可以从提供给该ART网络的一组输入数据生成聚类。每个簇代表观察到的ART网络的特定事物或事件的观察统计分布。

4. 发明授权

US08411935B2 Semantic representation module of a machine-learning engine in a video analysis system 有权
标题翻译：视频分析系统中机器学习引擎的语义表示模块
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。

5. 发明授权

US08358834B2 Background model for complex and dynamic scenes 有权
标题翻译：复杂和动态场景的背景模型
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。

6. 发明授权

US08280153B2 Visualizing and updating learned trajectories in video surveillance systems 有权
标题翻译：可视化和更新视频监控系统中的学习轨迹
公开(公告)号：US08280153B2
公开(公告)日：2012-10-02
申请号：US12543242
申请日：2009-08-18
申请人： Wesley Kenneth Cobb , Bobby Ernest Blythe , David Samuel Friedlander , Rajkiran Kumar Gottumukkal , Ming-Jung Seow , Gang Xu
发明人： Wesley Kenneth Cobb , Bobby Ernest Blythe , David Samuel Friedlander , Rajkiran Kumar Gottumukkal , Ming-Jung 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）。

7. 发明授权

US08270733B2 Identifying anomalous object types during classification 有权
标题翻译：在分类期间识别异常对象类型
公开(公告)号：US08270733B2
公开(公告)日：2012-09-18
申请号：US12551276
申请日：2009-08-31
申请人： Wesley Kenneth Cobb , David Friedlander , Rajkiran Kumar Gottumukkal , Ming-Jung Seow , Gang Xu
发明人： Wesley Kenneth Cobb , David Friedlander , Rajkiran Kumar Gottumukkal , Ming-Jung Seow , Gang Xu
IPC分类号： G06K9/62 , G01V3/00
CPC分类号： G06K9/6251 , G06K9/00771
摘要： Techniques are disclosed for identifying anomaly object types during classification of foreground objects extracted from image data. A self-organizing map and adaptive resonance theory (SOM-ART) network is used to discover object type clusters and classify objects depicted in the image data based on pixel-level micro-features that are extracted from the image data. Importantly, the discovery of the object type clusters 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. The SOM-ART network is adaptive and able to learn while discovering the object type clusters and classifying objects and identifying anomaly object types.
摘要翻译：公开了用于在从图像数据提取的前景对象的分类期间识别异常对象类型的技术。基于从图像数据提取的像素级微特征，使用自组织图和自适应共振理论（SOM-ART）网络来发现对象类型簇并对图像数据中描绘的对象进行分类。重要的是，对象类型簇的发现是无监督的，即独立于定义特定对象的任何训练数据执行，允许行为识别系统放弃训练阶段，并且对象分类进行而不受特定对象定义的约束。 SOM-ART网络是自适应的，能够在发现对象类型集群并分类对象并识别异常对象类型时学习。

8. 发明授权

US08121968B2 Long-term memory in a video analysis system 有权
标题翻译：视频分析系统的长期记忆
公开(公告)号：US08121968B2
公开(公告)日：2012-02-21
申请号：US12208551
申请日：2008-09-11
申请人： John Eric Eaton , Wesley Kenneth Cobb , Ming-Jung Seow , David Samuel Friedlander , Gang Xu
发明人： John Eric Eaton , Wesley Kenneth Cobb , Ming-Jung Seow , David Samuel Friedlander , Gang Xu
IPC分类号： G06F17/00 , G06F15/18 , G06N5/02 , G06K9/62 , G06K9/46 , G06K9/66
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。

9. 发明申请

US20110064267A1 CLASSIFIER ANOMALIES FOR OBSERVED BEHAVIORS IN A VIDEO SURVEILLANCE SYSTEM 有权
标题翻译：在视频监控系统中观察行为的分类器异常
公开(公告)号：US20110064267A1
公开(公告)日：2011-03-17
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
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。

10. 发明申请

US20110052068A1 IDENTIFYING ANOMALOUS OBJECT TYPES DURING CLASSIFICATION 有权
标题翻译：在分类期间识别异常对象类型
公开(公告)号：US20110052068A1
公开(公告)日：2011-03-03
申请号：US12551276
申请日：2009-08-31
申请人： WESLEY KENNETH COBB , David FRIEDLANDER , RAJKIRAN KUMAR GOTTUMUKKAL , MING-JUNG SEOW , GANG XU
发明人： WESLEY KENNETH COBB , David FRIEDLANDER , RAJKIRAN KUMAR GOTTUMUKKAL , MING-JUNG SEOW , GANG XU
IPC分类号： G06K9/46
CPC分类号： G06K9/6251 , G06K9/00771
摘要： Techniques are disclosed for identifying anomaly object types during classification of foreground objects extracted from image data. A self-organizing map and adaptive resonance theory (SOM-ART) network is used to discover object type clusters and classify objects depicted in the image data based on pixel-level micro-features that are extracted from the image data. Importantly, the discovery of the object type clusters 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. The SOM-ART network is adaptive and able to learn while discovering the object type clusters and classifying objects and identifying anomaly object types.
摘要翻译：公开了用于在从图像数据提取的前景对象的分类期间识别异常对象类型的技术。基于从图像数据提取的像素级微特征，使用自组织图和自适应共振理论（SOM-ART）网络来发现对象类型簇并对图像数据中描绘的对象进行分类。重要的是，对象类型簇的发现是无监督的，即独立于定义特定对象的任何训练数据执行，允许行为识别系统放弃训练阶段，并且对象分类进行而不受特定对象定义的约束。 SOM-ART网络是自适应的，能够在发现对象类型集群并分类对象并识别异常对象类型时学习。

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