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1. 发明授权

US09349054B1 Foreground detector for video analytics system 有权
标题翻译：视频分析系统前景探测器
公开(公告)号：US09349054B1
公开(公告)日：2016-05-24
申请号：US14526756
申请日：2014-10-29
申请人： Behavioral Recognition Systems, Inc.
发明人： Kishor Adinath Saitwal , Lon Risinger , Wesley Kenneth Cobb
IPC分类号： G06K9/34 , G06K9/00 , G06T7/00 , G06K9/62
CPC分类号： G06K9/00785 , G06T7/11 , G06T7/136 , G06T7/194 , G06T2207/10016
摘要： Techniques are disclosed for creating a background model of a scene using both a pixel based approach and a context based approach. The combined approach provides an effective technique for segmenting scene foreground from background in frames of a video stream. Further, this approach can scale to process large numbers of camera feeds simultaneously, e.g., using parallel processing architectures, while still generating an accurate background model. Further, using both a pixel based approach and context based approach ensures that the video analytics system can effectively and efficiently respond to changes in a scene, without overly increasing computational complexity. In addition, techniques are disclosed for updating the background model, from frame-to-frame, by absorbing foreground pixels into the background model via an absorption window, and dynamically updating background/foreground thresholds.
摘要翻译：公开了用于使用基于像素的方法和基于上下文的方法来创建场景的背景模型的技术。组合方法提供了一种用于从视频流的帧中的背景分割场景前景的有效技术。此外，该方法可以缩放以同时处理大量摄像机进给，例如使用并行处理架构，同时仍然产生准确的背景模型。此外，使用基于像素的方法和基于上下文的方法确保了视频分析系统可以有效地和有效地响应场景中的变化，而不会过度增加计算复杂性。此外，公开了通过经由吸收窗口将前景像素吸收到背景模型中并且动态地更新背景/前景阈值来从帧到帧更新背景模型的技术。

2. 发明授权

US09232140B2 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：US09232140B2
公开(公告)日：2016-01-05
申请号：US14077097
申请日：2013-11-11
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
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。

3. 发明授权

US09111148B2 Unsupervised learning of feature anomalies for a video surveillance system 有权
标题翻译：无监督学习视频监控系统的特征异常
公开(公告)号：US09111148B2
公开(公告)日：2015-08-18
申请号：US13929494
申请日：2013-06-27
申请人： BEHAVIORAL RECOGNITION SYSTEMS, Inc.
发明人： Ming-Jung Seow , Wesley Kenneth Cobb
IPC分类号： G06K9/46 , G06K9/62 , G06K9/00
CPC分类号： G06K9/00771 , G06K9/6218
摘要： Techniques are disclosed for analyzing a scene depicted in an input stream of video frames captured by a video camera. In one embodiment, e.g., a machine learning engine may include statistical engines for generating topological feature maps based on observations and a detection module for detecting feature anomalies. The statistical engines may include adaptive resonance theory (ART) networks which cluster observed position-feature characteristics. The statistical engines may further reinforce, decay, merge, and remove clusters. The detection module may calculate a rareness value relative to recurring observations and data in the ART networks. Further, the sensitivity of detection may be adjusted according to the relative importance of recently observed anomalies.
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。在一个实施例中，例如，机器学习引擎可以包括用于基于观察产生拓扑特征图的统计引擎和用于检测特征异常的检测模块。统计引擎可以包括聚类观察位置特征特征的自适应共振理论（ART）网络。统计引擎可能进一步加强，衰减，合并和删除群集。检测模块可以相对于ART网络中的重复观察和数据计算稀有度值。此外，检测的灵敏度可以根据最近观察到的异常的相对重要性进行调整。

4. 发明授权

US08548198B2 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：US08548198B2
公开(公告)日：2013-10-01
申请号：US13622281
申请日：2012-09-18
申请人： Behavioral Recognition Systems, Inc.
发明人： Wesley Kenneth Cobb , David Friedlander , Rajkiran Kumar Gottumukkal , Ming-Jung Seow , Gang Xu
IPC分类号： G06K9/00 , 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。

5. 发明授权

US09412027B2 Detecting anamolous sea-surface oil based on a synthetic discriminant signal and learned patterns of behavior 有权
标题翻译：基于合成判别信号和学习行为模式检测anamolous海面油
公开(公告)号：US09412027B2
公开(公告)日：2016-08-09
申请号：US14823771
申请日：2015-08-11
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）的视场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。

6. 发明授权

US09349275B2 Alert volume normalization in a video surveillance system 有权
标题翻译：视频监控系统中的警报音量正常化
公开(公告)号：US09349275B2
公开(公告)日：2016-05-24
申请号：US13836730
申请日：2013-03-15
申请人： Behavioral Recognition Systems, Inc.
发明人： Kishor Adinath Saitwal , Wesley Kenneth Cobb
IPC分类号： G06F17/00 , G06N5/02 , G08B23/00 , G06K9/00 , H04N7/00 , G06K9/32 , G08B13/196
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： Techniques are disclosed for normalizing and publishing alerts using a behavioral recognition-based video surveillance system configured with an alert normalization module. Certain embodiments allow a user of the behavioral recognition system to provide the normalization module with a set of relative weights for alert types and a maximum publication value. Using these values, the normalization module evaluates an alert and determines whether its rareness value exceed a threshold. Upon determining that the alert exceeds the threshold, the module normalizes and publishes the alert.
摘要翻译：公开了用于使用配置有警报规范化模块的基于行为识别的视频监控系统对警报进行归一化和发布的技术。某些实施例允许行为识别系统的用户向规范化模块提供警报类型和最大发布值的一组相对权重。使用这些值，归一化模块评估警报并确定其稀有度值是否超过阈值。当确定警报超过阈值时，模块规范化并发布警报。

7. 发明授权

US09208675B2 Loitering detection in a video surveillance system 有权
标题翻译：视频监控系统中的游荡检测
公开(公告)号：US09208675B2
公开(公告)日：2015-12-08
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： G06K9/00 , G06T7/20 , G08B13/196 , G08B23/00 , H04N7/00 , 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。

8. 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。

9. 发明授权

US09460522B2 Incremental update for background model thresholds 有权
标题翻译：背景模型阈值的增量更新
公开(公告)号：US09460522B2
公开(公告)日：2016-10-04
申请号：US14526879
申请日：2014-10-29
申请人： Behavioral Recognition Systems, Inc.
发明人： Kishor Adinath Saitwal , Lon Risinger , Wesley Kenneth Cobb
IPC分类号： G06K9/36 , G06T7/40 , G06T7/20 , G06K9/38 , G06K9/00
CPC分类号： G06T7/90 , G06K9/00624 , G06K9/38 , G06K9/40 , G06T7/194 , G06T7/215 , G06T7/254 , G06T2207/10016 , G06T2207/10024 , G06T2207/20036 , G06T2207/20076
摘要： Techniques are disclosed for creating a background model of a scene using both a pixel based approach and a context based approach. The combined approach provides an effective technique for segmenting scene foreground from background in frames of a video stream. Further, this approach can scale to process large numbers of camera feeds simultaneously, e.g., using parallel processing architectures, while still generating an accurate background model. Further, using both a pixel based approach and context based approach ensures that the video analytics system can effectively and efficiently respond to changes in a scene, without overly increasing computational complexity. In addition, techniques are disclosed for updating the background model, from frame-to-frame, by absorbing foreground pixels into the background model via an absorption window, and dynamically updating background/foreground thresholds.
摘要翻译：公开了用于使用基于像素的方法和基于上下文的方法来创建场景的背景模型的技术。组合方法提供了一种用于从视频流的帧中的背景分割场景前景的有效技术。此外，该方法可以缩放以同时处理大量摄像机进给，例如使用并行处理架构，同时仍然产生准确的背景模型。此外，使用基于像素的方法和基于上下文的方法确保了视频分析系统可以有效地和有效地响应场景中的变化，而不会过度增加计算复杂性。此外，公开了通过经由吸收窗口将前景像素吸收到背景模型中并且动态地更新背景/前景阈值来从帧到帧更新背景模型的技术。

10. 发明授权

US09104918B2 Method and system for detecting sea-surface oil 有权
标题翻译：海面油检测方法及系统
公开(公告)号：US09104918B2
公开(公告)日：2015-08-11
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。

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