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1. 发明申请

WO2011022275A3 ADAPTIVE VOTING EXPERTS FOR INCREMENTAL SEGMENTATION OF SEQUENCES WITH PREDICTION IN A VIDEO SURVEILLANCE SYSTEM 审中-公开
标题翻译：用于视频监控系统中预测序列增量分割的自适应表决专家
公开(公告)号：WO2011022275A3
公开(公告)日：2011-06-03
申请号：PCT/US2010045218
申请日：2010-08-11
申请人： BEHAVIORAL RECOGNITION SYS INC , COBB WESLEY KENNETH , BLYTHE BOBBY ERNEST , FRIEDLANDER DAVID SAMUEL , SAITWAL KISHOR ADINATH , XU GANG
发明人： COBB WESLEY KENNETH , BLYTHE BOBBY ERNEST , FRIEDLANDER DAVID SAMUEL , SAITWAL KISHOR ADINATH , XU GANG
IPC分类号： H04N7/18 , H04N5/262
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，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为在场景中观察到的不同对象分割自适应共振理论（ART）网络标签序列。序列层可以被配置为观察ART标签序列并递增地构建，更新和修剪以及为那些标签序列重组ngram字典。序列层计算ngram字典中节点的熵，并确定滑动窗口长度和投票计数参数。一旦确定，序列层可以分割新观察到的序列以估计场景中观察到的原始事件以及针对序列间序列异常和序列内异常发出警报。

2. 发明申请

WO2011022272A2 SCENE PRESET IDENTIFICATION USING QUADTREE DECOMPOSITION ANALYSIS 审中-公开
标题翻译：使用四分位分解分析的场景预设识别
公开(公告)号：WO2011022272A2
公开(公告)日：2011-02-24
申请号：PCT/US2010045206
申请日：2010-08-11
申请人： BEHAVIORAL RECOGNITION SYS INC , COBB WESLEY KENNETH , BLYTHE BOBBY ERNEST , GOTTUMUKKAL RAJKIRAN KUMAR , SAITWAL KISHOR ADINATH , XU GANG , YANG TAO
发明人： COBB WESLEY KENNETH , BLYTHE BOBBY ERNEST , GOTTUMUKKAL RAJKIRAN KUMAR , SAITWAL KISHOR ADINATH , XU GANG , YANG TAO
IPC分类号： H04N7/18 , H04N5/265
CPC分类号： G06K9/00771 , G06T7/254 , G06T2207/20081 , G06T2207/30232
摘要： Techniques are disclosed for matching a current background scene of an image received by a surveillance system with a gallery of scene presets that each represent a previously captured background scene. A quadtree decomposition analysis is used to improve the robustness of the matching operation when the scene lighting changes (including portions containing over-saturation/under-saturation) or a portion of the content changes. The current background scene is processed to generate a quadtree decomposition including a plurality of window portions. Each of the window portions is processed to generate a plurality of phase spectra. The phase spectra are then projected onto a corresponding plurality of scene preset image matrices of one or more scene preset. When a match between the current background scene and one of the scene presets is identified, the matched scene preset is updated. Otherwise a new scene preset is created based on the current background scene.
摘要翻译：公开了用于将由监视系统接收的图像的当前背景场景与每个表示先前捕获的背景场景的场景预设画廊进行匹配的技术。当场景照明改变（包括包含过饱和/欠饱和的部分）或内容的一部分改变时，使用四叉树分解分析来提高匹配操作的鲁棒性。处理当前背景场景以产生包括多个窗口部分的四叉树分解。每个窗口部分被处理以产生多个相位谱。然后将相位谱投影到一个或多个场景预设的相应的多个场景预设图像矩阵上。当当前背景场景与其中一个场景预设之间的匹配被识别时，匹配的场景预设被更新。否则，将根据当前的背景场景创建一个新的场景预设。

3. 发明申请

WO2008098188A2 BEHAVIORAL RECOGNITION SYSTEM 审中-公开
标题翻译：行为识别系统
公开(公告)号：WO2008098188A2
公开(公告)日：2008-08-14
申请号：PCT/US2008053457
申请日：2008-02-08
申请人： BEHAVIORAL RECOGNITION SYSTEMS , EATON JOHN ERIC , COBB WESLEY KENNETH , URECH DENNIS GENE , BLYTHE BOBBY ERNEST , FRIEDLANDER DAVID SAMUEL , GOTTUMUKKAL RAJKIRAN KUMAR , RISINGER LON WILLIAM , SAITWAL KISHOR ADINATH , SEOW MING-JUNG , SOLUM DAVID MARVIN , XU GANG , YANG TAO
发明人： EATON JOHN ERIC , COBB WESLEY KENNETH , URECH DENNIS GENE , BLYTHE BOBBY ERNEST , FRIEDLANDER DAVID SAMUEL , GOTTUMUKKAL RAJKIRAN KUMAR , RISINGER LON WILLIAM , SAITWAL KISHOR ADINATH , SEOW MING-JUNG , SOLUM DAVID MARVIN , XU GANG , YANG TAO
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用于基于所获取的视频帧流来分析和学习行为的方法和系统。流中描绘的对象是基于对视频帧的分析而确定的。每个对象可以具有用于跟踪对象的帧到帧的运动的对应搜索模型。对象的类被确定并且对象的语义表示被生成。语义表示用于确定对象的行为并学习在获取的视频流所描绘的环境中发生的行为。这样，系统通过分析环境中的动作或活动或不存在情况，迅速并实时地了解任何环境的正常和异常行为，并根据所了解的情况识别和预测异常和可疑行为。

4. 发明申请

WO2009009692A4 SEMANTIC REPRESENTATION MODULE OF A MACHINE-LEARNING ENGINE IN A VIDEO ANALYSIS SYSTEM 审中-公开
标题翻译：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：WO2009009692A4
公开(公告)日：2009-04-30
申请号：PCT/US2008069694
申请日：2008-07-10
申请人： BEHAVIORAL RECOGNITION SYSTEMS , EATON JOHN ERIC , COBB WESLEY KENNETH , URECH DENNIS G , FRIEDLANDER DAVID S , XU GANG , SEOW MING-JUNG , RISINGER LON W , SOLUM DAVID M , YANG TAO , GOTTUMUKKAL RAJKIRAN K , SAITWAL KISHOR ADINATH
发明人： EATON JOHN ERIC , COBB WESLEY KENNETH , URECH DENNIS G , FRIEDLANDER DAVID S , XU GANG , SEOW MING-JUNG , RISINGER LON W , SOLUM DAVID M , YANG TAO , GOTTUMUKKAL RAJKIRAN K , SAITWAL KISHOR ADINATH
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. 发明申请

WO2010030814A3 PIXEL-LEVEL BASED MICRO-FEATURE EXTRACTION 审中-公开
标题翻译：基于像素级的微特征提取
公开(公告)号：WO2010030814A3
公开(公告)日：2010-05-20
申请号：PCT/US2009056559
申请日：2009-09-10
申请人： BEHAVIORAL RECOGNITION SYSTEMS , COBB WESLEY KENNETH , GOTTUMUKKAL RAJKIRAN K , SAITWAL KISHOR ADINATH , SEOW MING-JUNG , XU GANG , RISINGER LON WILLIAM , GRAHAM JEFFREY
发明人： COBB WESLEY KENNETH , GOTTUMUKKAL RAJKIRAN K , SAITWAL KISHOR ADINATH , SEOW MING-JUNG , XU GANG , RISINGER LON WILLIAM , GRAHAM JEFFREY
IPC分类号： H04N5/262 , H04N5/76 , H04N7/18
CPC分类号： G06K9/3233 , G06K9/46 , 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.
摘要翻译：公开了用于基于一个或多个图像的特征在像素级提取微特征的技术。重要的是，提取是无监督的，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。不需要训练数据的微特征提取器在执行提取时是自适应和自动训练。提取的微特征被表示为可以被输入到微分类器的微特征向量，微分类器基于微特征向量将对象分组成对象类型聚类。

6. 发明申请

WO2009009692A3 SEMANTIC REPRESENTATION MODULE OF A MACHINE-LEARNING ENGINE IN A VIDEO ANALYSIS SYSTEM 审中-公开
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：WO2009009692A3
公开(公告)日：2009-03-12
申请号：PCT/US2008069694
申请日：2008-07-10
申请人： BEHAVIORAL RECOGNITION SYSTEMS , EATON JOHN ERIC , COBB WESLEY KENNETH , URECH DENNIS G , FRIEDLANDER DAVID S , XU GANG , SEOW MING-JUNG , RISINGER LON W , SOLUM DAVID M , YANG TAO , GOTTUMUKKAL RAJKIRAN K , SAITWAL KISHOR ADINATH
发明人： EATON JOHN ERIC , COBB WESLEY KENNETH , URECH DENNIS G , FRIEDLANDER DAVID S , XU GANG , SEOW MING-JUNG , RISINGER LON W , SOLUM DAVID M , YANG TAO , GOTTUMUKKAL RAJKIRAN K , SAITWAL KISHOR ADINATH
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，其被配置为通过分析随着时间的移动和/或活动（或者不存在）而识别和学习行为，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针对在视频帧序列中描绘的对象和相关矢量表示的原始事件序列和相关联的运动学数据。矢量表示从原始事件符号流和相空间符号流生成，并且流描述了在视频帧序列中描绘的对象的动作。

7. 发明申请

WO2009009697A4 COGNITIVE MODEL FOR A MACHINE-LEARNING ENGINE IN A VIDEO ANALYSIS SYSTEM 审中-公开
标题翻译：视频分析系统中机器学习发动机的认知模型
公开(公告)号：WO2009009697A4
公开(公告)日：2009-03-05
申请号：PCT/US2008069700
申请日：2008-07-10
申请人： BEHAVIORAL RECOGNITION SYSTEMS , EATON JOHN ERIC , COBB WESLEY KENNETH , URECH DENNIS G , FRIEDLANDER DAVID S , XU GANG , SEOW MING-JUNG , RISINGER LON W , SOLUM DAVID M , YANG TAO , GOTTUMUKKAL RAJKIRAN K , SAITWAL KISHOR ADINATH
发明人： EATON JOHN ERIC , COBB WESLEY KENNETH , URECH DENNIS G , FRIEDLANDER DAVID S , XU GANG , SEOW MING-JUNG , RISINGER LON W , SOLUM DAVID M , YANG TAO , GOTTUMUKKAL RAJKIRAN K , SAITWAL KISHOR ADINATH
IPC分类号： G06K9/00
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。

8. 发明申请

WO2011025662A2 DETECTING ANOMALOUS TRAJECTORIES IN A VIDEO SURVEILLANCE SYSTEM 审中-公开
标题翻译：在视频监控系统中检测异常TRAJECTORIES
公开(公告)号：WO2011025662A2
公开(公告)日：2011-03-03
申请号：PCT/US2010045229
申请日：2010-08-11
申请人： BEHAVIORAL RECOGNITION SYS INC , COBB WESLEY KENNETH , SEOW MING-JUNG , XU GANG
发明人： COBB WESLEY KENNETH , SEOW MING-JUNG , XU GANG
IPC分类号： G06T1/00 , G06F17/00 , G06T7/00
CPC分类号： G06K9/00771 , G06K9/00335 , G06K9/00718 , G06K9/6284 , G06T7/246 , G06T2207/10016 , G06T2207/10024 , G06T2207/30232 , G06T2207/30241
摘要： Techniques are disclosed for determining anomalous trajectories of objects tracked over a sequence of video frames. In one embodiment, a symbol trajectory may be derived from observing an object moving through a scene. The symbol trajectory represents semantic concepts extracted from the trajectory of the object. Whether the symbol trajectory is anomalous may be determined, based on previously observed symbol trajectories. A user may be alerted upon determining that the symbol trajectory is anomalous.
摘要翻译：公开了用于确定通过视频帧序列跟踪的对象的异常轨迹的技术。在一个实施例中，可以从观察通过场景移动的对象导出符号轨迹。符号轨迹表示从对象的轨迹中提取的语义概念。可以基于先前观察到的符号轨迹来确定符号轨迹是否是异常的。在确定符号轨迹是异常的时候可以警告用户。

9. 发明申请

WO2010030816A3 LONG-TERM MEMORY IN A VIDEO ANALYSIS SYSTEM 审中-公开
标题翻译：视频分析系统中的长期记忆
公开(公告)号：WO2010030816A3
公开(公告)日：2010-05-14
申请号：PCT/US2009056561
申请日：2009-09-10
申请人： BEHAVIORAL RECOGNITION SYSTEMS , EATON JOHN ERIC , COBB WESLEY KENNETH , SEOW MING-JUNG , FRIEDLANDER DAVID SAMUEL , XU GANG
发明人： EATON JOHN ERIC , COBB WESLEY KENNETH , SEOW MING-JUNG , FRIEDLANDER DAVID SAMUEL , XU GANG
IPC分类号： G06F15/00 , G06F9/06 , G06F12/00
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。随着时间的推移，长期记忆提供了一个容量随着时间的流逝而逐渐增大的记忆。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定义由长期记忆识别和操纵的多个模式，而是提供长期记忆，其中特征维度的大小（用于确定不同观察事件之间的相似性）可以动态地增长视需要而定，取决于在一系列视频帧中观察到的实际事件。

10. 发明申请

WO2010077979A3 DETECTING ANOMALOUS EVENTS USING A LONG-TERM MEMORY IN A VIDEO ANALYSIS SYSTEM 审中-公开
标题翻译：使用视频分析系统中的长期记忆检测异常事件
公开(公告)号：WO2010077979A3
公开(公告)日：2010-09-10
申请号：PCT/US2009068308
申请日：2009-12-16
申请人： BEHAVIORAL RECOGNITION SYSTEMS , COBB WESLEY KENNETH , SEOW MING-JUNG , XU GANG
发明人： COBB WESLEY KENNETH , SEOW MING-JUNG , XU GANG
IPC分类号： G06F17/00 , G06F17/10
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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