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11. 发明申请

US20170083790A1 DETECTED OBJECT TRACKER FOR A VIDEO ANALYTICS SYSTEM 审中-公开
公开(公告)号：US20170083790A1
公开(公告)日：2017-03-23
申请号：US14863344
申请日：2015-09-23
申请人： Behavioral Recognition Systems, Inc.
发明人： Lon RISINGER , Kishor Adinath SAITWAL , Wesley Kenneth COBB
IPC分类号： G06K9/62 , H04N7/18 , G06T7/40 , G06T7/20 , G06T1/20 , G06K9/00
CPC分类号： G06T1/20 , G06K9/00785 , G06T7/254 , G06T2207/10016 , G06T2207/10024 , G06T2207/30236 , G06T2207/30241
摘要： Techniques are disclosed which provide a detected object tracker for a video analytics system. As disclosed, the detected object tracker provides a robust foreground object tracking component for a video analytics system which allow other components of the video analytics system to more accurately evaluate the behavior of a given object (as well as to learn to identify different instances or occurrences of the same object) over time. More generally, techniques are disclosed for identifying what pixels of successive video frames depict the same foreground object. Logic implementing certain functions of the detected object tracker can be executed on either a conventional processor (e.g., a CPU) or a hardware acceleration processing device (e.g., a GPU), allowing multiple camera feeds to be evaluated in parallel.

12. 发明授权

US09507768B2 Cognitive information security using a behavioral recognition system 有权
标题翻译：使用行为识别系统的认知信息安全
公开(公告)号：US09507768B2
公开(公告)日：2016-11-29
申请号：US14457060
申请日：2014-08-11
申请人： Behavioral Recognition Systems, Inc.
发明人： Wesley Kenneth Cobb , Ming-Jung Seow , Curtis Edward Cole, Jr. , Cody Shay Falcon , Benjamin A. Konosky , Charles Richard Morgan , Aaron Poffenberger , Thong Toan Nguyen
IPC分类号： G06F17/27 , H04L29/06 , G06N99/00
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 a method for processing streams of data of one or more networked computer systems. According to one embodiment of the present disclosure, an ordered stream of normalized vectors corresponding to information security data obtained from one or more sensors monitoring a computer network is received. A neuro-linguistic model of the information security data is generated by clustering the ordered stream of vectors and assigning a letter to each cluster, outputting an ordered sequence of letters based on a mapping of the ordered stream of normalized vectors to the clusters, building a dictionary of words from of the ordered output of letters, outputting an ordered stream of words based on the ordered output of letters, and generating a plurality of phrases based on the ordered output of words.
摘要翻译：本文提出的实施例描述了一种用于处理一个或多个联网的计算机系统的数据流的方法。根据本公开的一个实施例，接收与从监视计算机网络的一个或多个传感器获得的信息安全数据相对应的归一化向量的有序流。信息安全数据的神经语言模型是通过将有序的向量流聚类并分配给每个聚类的一个字母来生成的，其基于归一化向量的有序流向集群的映射输出有序的字母序列，构建一个根据有序输出的字母的字的词典，基于字母的有序输出输出有序的单词流，并且基于单词的有序输出生成多个短语。

13. 发明申请

US20160232652A1 AUTOMATIC GAIN CONTROL FILTER IN A VIDEO ANALYSIS SYSTEM 审中-公开
标题翻译：视频分析系统中的自动增益控制滤波器
公开(公告)号：US20160232652A1
公开(公告)日：2016-08-11
申请号：US15132670
申请日：2016-04-19
申请人： BEHAVIORAL RECOGNITION SYSTEMS, Inc.
发明人： Ming-Jung SEOW , Tao YANG , Wesley Kenneth COBB
IPC分类号： G06T5/00 , G06K9/62 , G06T7/00
CPC分类号： G06T5/009 , G06K9/6218 , G06K9/6267 , G06T5/008 , G06T7/10 , G06T7/194 , G06T7/251 , G06T2207/10016 , G06T2207/10024 , G06T2207/20081 , G06T2207/20208
摘要： Techniques are disclosed for analyzing a scene depicted in an input stream of video frames captured by a video camera. Bounding boxes are determined for a set foreground patches identified in a video frame. For each bounding box, the techniques include determining textures for first areas, each including a foreground pixel and surrounding pixels, and determining textures for second areas including pixels of the background model image corresponding to the pixels of the foreground areas. Further, for each foreground pixel in the bounding box area, a correlation score is determined based on the texture of the corresponding first area and second area. Pixels whose correlation scores exceed a threshold are removed from the foreground patch. The size of the bounding box may also be reduced to fit the modified foreground patch.
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。确定在视频帧中标识的设置的前景色块的边界框。对于每个边界框，技术包括确定第一区域的纹理，每个区域包括前景像素和周围像素，以及确定包括与前景区域的像素对应的背景模型图像的像素的第二区域的纹理。此外，对于边界区域中的每个前景像素，基于对应的第一区域和第二区域的纹理来确定相关得分。相关性分数超过阈值的像素从前景补丁中删除。边界框的大小也可以减小以适应修改的前景块。

14. 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。

15. 发明申请

US20160134812A1 IMAGE STABILIZATION TECHNIQUES FOR VIDEO SURVEILLANCE SYSTEMS 有权
标题翻译：视频监控系统的图像稳定技术
公开(公告)号：US20160134812A1
公开(公告)日：2016-05-12
申请号：US14988475
申请日：2016-01-05
申请人： Behavioral Recognition Systems, Inc.
发明人： Kishor Adinath SAITWAL , Wesley Kenneth COBB , Tao YANG
IPC分类号： H04N5/232 , G06T7/20 , H04N5/77
CPC分类号： H04N5/23267 , G06T7/246 , G06T2207/10016 , G06T2207/20021 , G06T2207/30232 , H04N5/23254 , H04N5/77
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 provide image stabilization of a video stream obtained from a camera. An image stabilization module in the behavioral recognition system obtains a reference image from the video stream. The image stabilization module identifies alignment regions within the reference image based on the regions of the image that are dense with features. Upon determining that the tracked features of a current image is out of alignment with the reference image, the image stabilization module uses the most feature dense alignment region to estimate an affine transformation matrix to apply to the entire current image to warp the image into proper alignment.
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。某些实施例可以提供从相机获得的视频流的图像稳定性。行为识别系统中的图像稳定模块从视频流中获得参考图像。图像稳定模块基于具有特征密集的图像的区域来识别参考图像内的对准区域。在确定当前图像的跟踪特征与参考图像不对齐时，图像稳定模块使用最特征密集对准区域来估计仿射变换矩阵以应用于整个当前图像以将图像扭曲成适当的对准。

16. 发明申请

US20150078656A1 VISUALIZING AND UPDATING LONG-TERM MEMORY PERCEPTS IN A VIDEO SURVEILLANCE SYSTEM 审中-公开
标题翻译：在视频监控系统中实现和更新长期记忆
公开(公告)号：US20150078656A1
公开(公告)日：2015-03-19
申请号：US14337703
申请日：2014-07-22
申请人： Behavioral Recognition Systems, Inc.
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： G06K9/6217 , G06K9/00771 , G06K9/00973 , H04N7/18 , H04N7/183
摘要： Techniques are disclosed for visually conveying a percept. The percept may represent information learned by a video surveillance system. A request may be received to view a percept for a specified scene. The percept may have been derived from data streams generated from a sequence of video frames depicting the specified scene captured by a video camera. A visual representation of the percept may be generated. A user interface may be configured to display the visual representation of the percept and to allow a user to view and/or modify metadata attributes with the percept. For example, the user may label a percept and set events matching the percept to always (or never) result in alert being generated for users of the video surveillance system.
摘要翻译：公开了用于视觉地传达感知的技术。感知可以表示视频监控系统学到的信息。可以接收请求以查看指定场景的感知。感知可能是从描绘由摄像机捕获的指定场景的视频帧序列产生的数据流导出的。可以产生视觉的视觉表示。用户界面可以被配置为显示感知的视觉表示，并允许用户使用感知来查看和/或修改元数据属性。例如，用户可以标记感知并将匹配感知的事件设置为始终（或从不）导致为视频监控系统的用户生成警报。

17. 发明申请

US20150047040A1 COGNITIVE INFORMATION SECURITY USING A BEHAVIORAL RECOGNITION SYSTEM 有权
标题翻译：使用行为识别系统的认知信息安全
公开(公告)号：US20150047040A1
公开(公告)日：2015-02-12
申请号：US14457060
申请日：2014-08-11
申请人： Behavioral Recognition Systems, Inc.
发明人： Wesley Kenneth COBB , Ming-Jung SEOW , Curtis Edward COLE, JR. , Cody Shay FALCON , Benjamin A. KONOSKY , Charles Richard MORGAN , Aaron POFFENBERGER , Thong Toan NGUYEN
IPC分类号： H04L29/06 , 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 a method for processing streams of data of one or more networked computer systems. According to one embodiment of the present disclosure, an ordered stream of normalized vectors corresponding to information security data obtained from one or more sensors monitoring a computer network is received. A neuro-linguistic model of the information security data is generated by clustering the ordered stream of vectors and assigning a letter to each cluster, outputting an ordered sequence of letters based on a mapping of the ordered stream of normalized vectors to the clusters, building a dictionary of words from of the ordered output of letters, outputting an ordered stream of words based on the ordered output of letters, and generating a plurality of phrases based on the ordered output of words.
摘要翻译：本文提出的实施例描述了一种用于处理一个或多个联网的计算机系统的数据流的方法。根据本公开的一个实施例，接收与从监视计算机网络的一个或多个传感器获得的信息安全数据相对应的归一化向量的有序流。信息安全数据的神经语言模型是通过将有序的向量流聚类并分配给每个聚类的一个字母来生成的，其基于归一化向量的有序流向集群的映射输出有序的字母序列，构建一个根据有序输出的字母的字的词典，基于字母的有序输出输出有序的单词流，并且基于单词的有序输出生成多个短语。

18. 发明申请

US20140050355A1 METHOD AND SYSTEM FOR DETECTING SEA-SURFACE OIL 有权
标题翻译：用于检测海表油的方法和系统
公开(公告)号：US20140050355A1
公开(公告)日：2014-02-20
申请号：US13971027
申请日：2013-08-20
申请人： Behavioral Recognition Systems, Inc.
发明人： Wesley Kenneth COBB
IPC分类号： G06K9/00
CPC分类号： G06T7/0004 , G06K9/00664 , G06K9/00718 , G06K9/00771 , G06T7/10 , G06T7/194 , G06T2207/30108
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 detect and evaluate the presence of sea-surface oil on the water surrounding an offshore oil platform. The computer vision engine may be configured to segment image data into detected patches or blobs of surface oil (foreground) present in the field of view of an infrared camera (or cameras). A machine learning engine may evaluate the detected patches of surface oil to learn to distinguish between sea-surface oil incident to the operation of an offshore platform and the appearance of surface oil that should be investigated by platform personnel.
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检测和评估在海上石油平台周围的水面上的海面油的存在。计算机视觉引擎可以被配置为将图像数据分割成在红外线照相机（或照相机）的视野中存在的检测到的表面油（前景）的斑块或斑块。机器学习引擎可以评估检测到的地表油块，以学习区分海上平台操作的海面油和平台人员应该调查的表面油的外观。

19. 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。

20. 发明申请

US20130121533A1 INTER-TRAJECTORY ANOMALY DETECTION USING ADAPTIVE VOTING EXPERTS IN A VIDEO SURVEILLANCE SYSTEM 审中-公开
标题翻译：在视频监控系统中使用自适应投票专家进行异地检测
公开(公告)号：US20130121533A1
公开(公告)日：2013-05-16
申请号：US13722812
申请日：2012-12-20
申请人： BEHAVIORAL RECOGNITION SYSTEMS, INC.
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。

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