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1. 发明申请

US20170083765A1 DETECTED OBJECT TRACKER FOR A VIDEO ANALYTICS SYSTEM 审中-公开
公开(公告)号：US20170083765A1
公开(公告)日：2017-03-23
申请号：US14863295
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： G06K9/00 , G06T7/00 , G06T3/00 , G06K9/62
CPC分类号： G06K9/00624 , G06K9/6202 , G06K9/6215 , G06K9/6267 , G06T7/11 , G06T7/194 , G06T7/251 , G06T2207/10016 , G06T2207/10024 , G06T2207/20081 , 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.

2. 发明申请

US20160267777A1 ALERT VOLUME NORMALIZATION IN A VIDEO SURVEILLANCE SYSTEM 审中-公开
公开(公告)号：US20160267777A1
公开(公告)日：2016-09-15
申请号：US15163461
申请日：2016-05-24
申请人： Behavioral Recognition Systems, Inc.
发明人： Wesley Kenneth COBB , Kishor Adinath SAITWAL
IPC分类号： G08B29/18 , G06K9/00 , G06K9/52 , G08B21/18
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.

3. 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。

4. 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.

5. 发明申请

US20160125621A1 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：US20160125621A1
公开(公告)日：2016-05-05
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： G06T7/40 , G06K9/38 , G06K9/00 , G06T7/20
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。

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

US20150003671A1 ANOMALOUS OBJECT INTERACTION DETECTION AND REPORTING 有权
标题翻译：异常对象相互作用检测和报告
公开(公告)号：US20150003671A1
公开(公告)日：2015-01-01
申请号：US13931058
申请日：2013-06-28
申请人： BEHAVIORAL RECOGNITION SYSTEMS, Inc.
发明人： Kishor Adinath SAITWAL , Dennis G. URECH , Wesley Kenneth COBB
IPC分类号： G06K9/00 , G06T7/00
CPC分类号： G06K9/00711 , G06K9/6221 , G06K9/6284
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 evaluating sequence pairs representing segments of object trajectories. Assuming the objects interact, each of the sequences of the sequence pair may be mapped to a sequence cluster of an adaptive resonance theory (ART) network. A rareness value for the pair of sequence clusters may be determined based on learned joint probabilities of sequence cluster pairs. A statistical anomaly model, which may be specific to an interaction type or general to a plurality of interaction types, is used to determine an anomaly temperature, and alerts are issued based at least on the anomaly temperature. In addition, the ART network and the statistical anomaly model are updated based on the current interaction.
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。这些技术包括评估表示物体轨迹段的序列对。假设对象相互作用，序列对中的每个序列可以被映射到自适应共振理论（ART）网络的序列簇。可以基于序列簇对的学习联合概率来确定该对序列簇的稀有度值。可以使用统计异常模型，其可以特定于交互类型或一般到多个交互类型，以确定异常温度，并且至少基于异常温度发出警报。此外，ART网络和统计异常模型基于当前的交互更新。

8. 发明申请

US20140132786A1 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：US20140132786A1
公开(公告)日：2014-05-15
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
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。

9. 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.

10. 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。

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