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1. 发明申请

US20160170964A1 LEXICAL ANALYZER FOR A NEURO-LINGUISTIC BEHAVIOR RECOGNITION SYSTEM 审中-公开
标题翻译：神经元行为识别系统的分析仪
公开(公告)号：US20160170964A1
公开(公告)日：2016-06-16
申请号：US14569104
申请日：2014-12-12
申请人： Behavioral Recognition Systems, Inc.
发明人： Gang XU , Ming-Jung SEOW , Tao YANG , Wesley Kenneth COBB
IPC分类号： G06F17/27 , G06F17/28
CPC分类号： G06F17/2735 , G06F17/2775 , G06N5/045 , G06N20/00
摘要： Techniques are disclosed for building a dictionary of words from combinations of symbols generated based on input data. A neuro-linguistic behavior recognition system includes a neuro-linguistic module that generates a linguistic model that describes data input from a source (e.g., video data, SCADA data, etc.). To generate words for the linguistic model, a lexical analyzer component in the neuro-linguistic module receives a stream of symbols, each symbol generated based on an ordered stream of normalized vectors generated from input data. The lexical analyzer component determines words from combinations of the symbols based on a hierarchical learning model having one or more levels. Each level indicates a length of the words to be identified at that level. Statistics are evaluated for the words identified at each level. The lexical analyzer component identifies one or more of the words having statistical significance.
摘要翻译：公开了用于从基于输入数据生成的符号的组合构建词典的技术。神经语言行为识别系统包括产生描述从源输入的数据（例如，视频数据，SCADA数据等）的语言模型的神经语言模块。为了生成语言模型的单词，神经语言模块中的词汇分析器组件接收符号流，每个符号基于从输入数据生成的归一化向量的有序流而产生。词汇分析器组件基于具有一个或多个级别的分级学习模型来确定符号的组合中的单词。每个级别表示要在该级别识别的单词的长度。对每个级别识别的单词进行统计学评估。词汇分析器组件识别具有统计学意义的一个或多个词。

2. 发明申请

US20150110388A1 SEMANTIC REPRESENTATION MODULE OF A MACHINE-LEARNING ENGINE IN A VIDEO ANALYSIS SYSTEM 有权
公开(公告)号：US20150110388A1
公开(公告)日：2015-04-23
申请号：US14584967
申请日：2014-12-29
申请人： Behavioral Recognition Systems, Inc.
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： G06K9/00 , G06N99/00 , G06K9/66 , G06F17/30
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.

3. 发明申请

US20170148183A1 IMAGE DRIVER THAT SAMPLES HIGH-RESOLUTION IMAGE DATA 审中-公开
公开(公告)号：US20170148183A1
公开(公告)日：2017-05-25
申请号：US14952090
申请日：2015-11-25
申请人： Behavioral Recognition Systems, Inc.
发明人： Kishor Adinath SAITWAL , Lon W. RISINGER , Wesley Kenneth COBB , Ming-Jung SEOW , Gang XU
IPC分类号： G06T7/60 , G06K9/00 , G06T7/40 , G06K9/46 , G06K9/52 , H04N5/33
CPC分类号： G06T7/60 , G06K9/00744 , G06K9/00771 , G06K9/2054 , G06K9/46 , G06K9/4652 , G06K9/52 , G06K2009/4666 , G06T7/0002 , G06T2207/10016 , G06T2207/10024 , G06T2207/20081 , G06T2207/20084 , G06T2207/30232 , H04N5/33
摘要： Techniques are disclosed for generating a low-dimensional representation of an image. An image driver receives an image captured by a camera. The image includes features based on pixel values in the image, and each feature describes the image in one or more image regions. The image driver generates, for each of the plurality of features, a feature vector that includes values for that feature corresponding to at least one of the image regions. Each value indicates a degree that the feature is present in the image region. The image driver generates a sample vector from each of the feature vectors. The sample vector includes each of the values included in the generated feature vectors.

4. 发明申请

US20160171096A1 MAPPER COMPONENT FOR A NEURO-LINGUISTIC BEHAVIOR RECOGNITION SYSTEM 审中-公开
标题翻译：神经元行为识别系统的MAPPER组件
公开(公告)号：US20160171096A1
公开(公告)日：2016-06-16
申请号：US14569034
申请日：2014-12-12
申请人： Behavioral Recognition Systems, Inc.
发明人： Ming-Jung SEOW , Gang XU , Tao YANG , Wesley Kenneth COBB
IPC分类号： G06F17/30
CPC分类号： G06N7/005 , G06K9/42 , G06K9/6218 , G06K9/6251 , G06K9/6255 , G06K9/723
摘要： Techniques are disclosed for generating a sequence of symbols based on input data for a neuro-linguistic model. The model may be used by a behavior recognition system to analyze the input data. A mapper component of a neuro-linguistic module in the behavior recognition system receives one or more normalized vectors generated from the input data. The mapper component generates one or more clusters based on a statistical distribution of the normalized vectors. The mapper component evaluates statistics and identifies statistically relevant clusters. The mapper component assigns a distinct symbol to each of the identified clusters.
摘要翻译：公开了用于基于神经语言学模型的输入数据生成符号序列的技术。模型可以被行为识别系统用于分析输入数据。行为识别系统中的神经语言模块的映射器组件接收从输入数据生成的一个或多个归一化矢量。映射器组件基于归一化向量的统计分布生成一个或多个簇。映射器组件评估统计信息并识别统计相关的集群。映射器组件为每个识别的集群分配一个不同的符号。

5. 发明申请

US20130242093A1 ALERT DIRECTIVES AND FOCUSED ALERT DIRECTIVES IN A BEHAVIORAL RECOGNITION SYSTEM 审中-公开
标题翻译：行为认知系统中的警告指令和聚焦警告指令
公开(公告)号：US20130242093A1
公开(公告)日：2013-09-19
申请号：US13839587
申请日：2013-03-15
申请人： BEHAVIORAL RECOGNITION SYSTEMS, INC.
发明人： Wesley Kenneth COBB , Ming-Jung SEOW , Gang XU , Kishor Adinath SAITWAL , Anthony AKINS , Kerry JOSEPH , Dennis G. URECH
IPC分类号： H04N7/00
CPC分类号： G08B29/185 , G06K9/00369 , G06K9/00771 , G06K9/00778 , G06K9/3241 , G06K9/52 , G06K9/6254 , G08B13/19608 , G08B21/182 , G08B23/00 , H04N7/002
摘要： Alert directives and focused alert directives allow a user to provide feedback to a behavioral recognition system to always or never publish an alert for certain events. Such an approach bypasses the normal publication methods of the behavioral recognition system yet does not obstruct the system's learning procedures.
摘要翻译：警报指令和集中的警报指令允许用户向行为识别系统提供反馈，以始终或永远不会发布某些事件的警报。这种方法绕过了行为识别系统的正常出版方法，但并不妨碍系统的学习过程。

6. 发明申请

US20150046155A1 COGNITIVE NEURO-LINGUISTIC BEHAVIOR RECOGNITION SYSTEM FOR MULTI-SENSOR DATA FUSION 有权
标题翻译：用于多传感器数据融合的认知神经元行为识别系统
公开(公告)号：US20150046155A1
公开(公告)日：2015-02-12
申请号：US14457082
申请日：2014-08-11
申请人： Behavioral Recognition Systems, Inc.
发明人： Ming-Jung SEOW , Wesley Kenneth COBB , Gang XU , Tao YANG , Aaron POFFENBERGER , Lon W. RISINGER , Kishor Adinath SAITWAL , Michael S. YANTOSCA , David M. SOLUM , Alex David HEMSATH , Dennis G. URECH , Duy Trong NGUYEN , Charles Richard MORGAN
IPC分类号： G06F17/27
CPC分类号： H04L63/1425 , G06F17/2725 , G06F17/2735 , G06F17/274 , G06F17/277 , G06F17/2775 , G06F17/2785 , G06F17/2795 , G06F17/28 , G06N99/005 , H04L63/1408
摘要： Embodiments presented herein describe techniques for generating a linguistic model of input data obtained from a data source (e.g., a video camera). According to one embodiment of the present disclosure, a sequence of symbols is generated based on an ordered stream of normalized vectors generated from the input data. A dictionary of words is generated from combinations of the ordered sequence of symbols based on a frequency at which combinations of symbols appear in the ordered sequence of symbols. A plurality of phrases is generated based an ordered sequence of words from the dictionary observed in the ordered sequence of symbols based on a frequency by which combinations of words in ordered sequence of words appear relative to one another.
摘要翻译：本文提出的实施例描述了用于生成从数据源（例如，摄像机）获得的输入数据的语言模型的技术。根据本公开的一个实施例，基于从输入数据生成的归一化矢量的有序流来生成符号序列。基于符号序列组合出现在有序序列符号中的频率，从有序符号序列的组合生成词典。基于在有序序列中的单词的组合相对于彼此出现的频率，基于在有序序列序列中观察到的字典的有序序列序列生成多个短语。

7. 发明申请

US20160170961A1 PERCEPTUAL ASSOCIATIVE MEMORY FOR A NEURO-LINGUISTIC BEHAVIOR RECOGNITION SYSTEM 审中-公开
标题翻译：神经行为行为识别系统的相关记忆
公开(公告)号：US20160170961A1
公开(公告)日：2016-06-16
申请号：US14569161
申请日：2014-12-12
申请人： Behavioral Recognition Systems, Inc.
发明人： Ming-Jung SEOW , Gang XU , Tao YANG , Wesley Kenneth COBB
IPC分类号： G06F17/27 , G06F17/28
CPC分类号： G06F17/277 , G06F17/2735 , G06F17/28
摘要： Techniques are disclosed for generating a syntax for a neuro-linguistic model of input data obtained from one or more sources. A stream of words of a dictionary built from a sequence of symbols are received. The symbols are generated from an ordered stream of normalized vectors generated from input data. Statistics for combinations of words co-occurring in the stream are evaluated. The statistics includes a frequency upon which the combinations of words co-occur. A model of combinations of words based on the evaluated statistics is updated. The model identifies statistically relevant words. A connected graph is generated. Each node in the connected graph represents one of the words in the stream. Edges connecting the nodes represent a probabilistic relationship between words in the stream. Phrases are identified based on the connected graph.
摘要翻译：公开了用于产生用于从一个或多个源获得的输入数据的神经语言模型的语法的技术。从符号序列构建的字典的流被接收。符号从从输入数据生成的归一化矢量的有序流中生成。评估在流中共同出现的词组合的统计。统计信息包括词组合在一起的频率。基于评估统计的单词组合模型被更新。该模型识别统计学上相关的词。生成连接图。连接图中的每个节点表示流中的一个单词。连接节点的边表示流中的单词之间的概率关系。短语基于连接的图形来识别。

8. 发明申请

US20130243252A1 LOITERING DETECTION IN A VIDEO SURVEILLANCE SYSTEM 有权
标题翻译：视频监控系统中的LOITERING检测
公开(公告)号：US20130243252A1
公开(公告)日：2013-09-19
申请号：US13836372
申请日：2013-03-15
申请人： BEHAVIORAL RECOGNITION SYSTEMS, INC.
发明人： Gang XU , Wesley Kenneth COBB
IPC分类号： G06K9/32
CPC分类号： G08B29/185 , G06K9/00369 , G06K9/00771 , G06K9/00778 , G06K9/3241 , G06K9/52 , G06K9/6254 , G08B13/19608 , G08B21/182 , G08B23/00 , H04N7/002
摘要： A behavioral recognition system may include both a computer vision engine and a machine learning engine configured to observe and learn patterns of behavior in video data. Certain embodiments may be configured to learn patterns of behavior consistent with a person loitering and generate alerts for same. Upon receiving information of a foreground object remaining in a scene over a threshold period of time, a loitering detection module evaluates the whether the object trajectory corresponds to a random walk. Upon determining that the trajectory does correspond, the loitering detection module generates a loitering alert.
摘要翻译：行为识别系统可以包括计算机视觉引擎和被配置为观察和学习视频数据中的行为模式的机器学习引擎。某些实施例可以被配置为学习与人游荡相一致的行为模式，并为其产生警报。一旦在阈值时间段内接收到场景中剩余的前景物体的信息，则游荡检测模块评估对象轨迹是否对应于随机游走。在确定轨迹确实对应时，游荡检测模块产生漂流警报。

9. 发明申请

US20140072206A1 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发动机的语义表示模块
公开(公告)号：US20140072206A1
公开(公告)日：2014-03-13
申请号：US13855332
申请日：2013-04-02
申请人： Behavioral Recognition Systems, Inc.
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： G06K9/66 , 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。

10. 发明申请

US20140002647A1 ANOMALOUS STATIONARY OBJECT DETECTION AND REPORTING 有权
标题翻译：异常静止对象检测和报告
公开(公告)号：US20140002647A1
公开(公告)日：2014-01-02
申请号：US13930190
申请日：2013-06-28
申请人： BEHAVIORAL RECOGNITION SYSTEMS, Inc.
发明人： Gang XU , Wesley Kenneth COBB
IPC分类号： H04N7/18
CPC分类号： H04N7/18 , G06K9/6284 , H04N7/188
摘要： Techniques are disclosed for analyzing a scene depicted in an input stream of video frames captured by a video camera. The techniques include receiving data for an object within the scene and determining whether the object has remained substantially stationary within the scene for at least a threshold period. If the object is determined to have remained stationary for at least the threshold period, a rareness score is calculated for the object to indicate a likelihood of the object being stationary to the observed degree at the observed location. The rareness score may use a learning model to take into account previous stationary and/or non-stationary behavior of objects within the scene. In general, the learning model may be updated based on observed stationary and/or non-stationary behaviors of the objects. If the rareness score meets reporting conditions, the stationary object event may be reported.
摘要翻译：公开了用于分析由摄像机捕获的视频帧的输入流中描绘的场景的技术。这些技术包括接收场景内的对象的数据，并确定对象是否在场景内基本上保持静止至少一个阈值周期。如果物体被确定为在至少阈值周期内保持静止，则为对象计算稀有度得分，以指示物体在观察位置处观察到的度数的稳定性的可能性。稀有度分数可以使用学习模型来考虑场景内的对象的先前的静止和/或非平稳的行为。通常，可以基于观察到的物体的静止和/或非静止行为来更新学习模型。如果稀有度得分满足报告条件，可能会报告静止物体事件。

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