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1. 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。

2. 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。

3. 发明授权

US09113143B2 Detecting and responding to an out-of-focus camera in a video analytics system 有权
标题翻译：检测和响应视频分析系统中的离焦相机
公开(公告)号：US09113143B2
公开(公告)日：2015-08-18
申请号：US13930958
申请日：2013-06-28
申请人： BEHAVIORAL RECOGNITION SYSTEMS, Inc.
发明人： Ming-Jung Seow , Dennis G. Urech
IPC分类号： G06K9/00 , H04N17/00 , G06T7/00 , H04N5/235
CPC分类号： H04N17/002 , G06T7/11 , G06T7/13 , G06T7/194 , G06T2207/10016
摘要： Techniques are disclosed for detecting an out-of-focus camera in a video analytics system. In one embodiment, a preprocessor component performs a pyramid image decomposition on a video frame captured by a camera. The preprocessor further determines sharp edge areas, candidate blurry edge areas, and actual blurry edge areas, in each level of the pyramid image decomposition. Based on the sharp edge areas, the candidate blurry edge areas, and actual blurry edge areas, the preprocessor determines a sharpness value and a blurriness value which indicate the overall sharpness and blurriness of the video frame, respectively. Based on the sharpness value and the blurriness value, the preprocessor further determines whether the video frame is out-of-focus and whether to send the video frame to components of a computer vision engine and/or a machine learning engine.
摘要翻译：公开了用于检测视频分析系统中的离焦相机的技术。在一个实施例中，预处理器组件对由相机拍摄的视频帧执行金字塔图像分解。预处理器还在金字塔图像分解的每个级别中确定尖锐边缘区域，候选模糊边缘区域和实际模糊边缘区域。基于尖锐边缘区域，候选模糊边缘区域和实际模糊边缘区域，预处理器分别确定表示视频帧的整体清晰度和模糊度的清晰度值和模糊度值。基于锐度值和模糊度值，预处理器进一步确定视频帧是否失焦，以及是否将视频帧发送到计算机视觉引擎和/或机器学习引擎的组件。

4. 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。

5. 发明授权

US09317908B2 Automatic gain control filter in a video analysis system 有权
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：US09317908B2
公开(公告)日：2016-04-19
申请号：US13930222
申请日：2013-06-28
申请人： BEHAVIORAL RECOGNITION SYSTEMS, Inc.
发明人： Ming-Jung Seow , Tao Yang , Wesley Kenneth Cobb
IPC分类号： G06K9/34 , G06T5/00 , G06T7/20
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。

6. 发明授权

US09111353B2 Adaptive illuminance filter in a video analysis system 有权
标题翻译：视频分析系统中的自适应照度滤波器
公开(公告)号：US09111353B2
公开(公告)日：2015-08-18
申请号：US13930276
申请日：2013-06-28
申请人： BEHAVIORAL RECOGNITION SYSTEMS, Inc.
发明人： Ming-Jung Seow , Tao Yang , Wesley Kenneth Cobb
IPC分类号： G06K9/46 , H04N9/74 , G06T7/00 , G06K9/00 , G06T7/20
CPC分类号： G06T7/0079 , G06K9/00771 , G06T7/254 , G06T2207/10016 , G06T2207/10024 , G06T2207/20081 , G06T2207/20084 , G06T2207/30232
摘要： Techniques are disclosed for removing false-positive foreground pixels resulting from environmental illumination effects. The techniques include receiving a foreground image and a background model, and determining an approximated reflectance component of the foreground image based on the foreground image itself and a background model image which is used as a proxy for an illuminance component of the foreground image. Pixels of the foreground image having approximated reflectance values less than a threshold value may be classified as false-positive foreground pixels and removed from the foreground image. Further, the threshold value used may be adjusted based on various factors to account for, e.g., different illumination conditions indoors and outdoors.
摘要翻译：公开了用于消除由环境照明效应产生的假阳性前景像素的技术。这些技术包括接收前景图像和背景模型，以及基于前景图像本身和用作前景图像的照度分量的代理的背景模型图像来确定前景图像的近似反射分量。具有小于阈值的近似反射率值的前景图像的像素可以被分类为假正前景像素并从前景图像中去除。此外，所使用的阈值可以基于各种因素进行调整，以解决例如室内和室外的不同照明条件。

7. 发明授权

US08923609B2 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：US08923609B2
公开(公告)日：2014-12-30
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： G06K9/62 , G06K9/48 , G06K9/00 , G06K9/66
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。

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