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31. 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。

32. 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。

33. 发明申请

US20150347856A1 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：US20150347856A1
公开(公告)日：2015-12-03
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。

34. 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。

35. 发明申请

US20140015984A1 DETECTING AND RESPONDING TO AN OUT-OF-FOCUS CAMERA IN A VIDEO ANALYTICS SYSTEM 有权
标题翻译：在视频分析系统中检测和响应非聚焦摄像机
公开(公告)号：US20140015984A1
公开(公告)日：2014-01-16
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： H04N17/00
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。

36. 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。

37. 发明申请

US20130022242A1 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：US20130022242A1
公开(公告)日：2013-01-24
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： G06K9/80
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。

38. 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.

39. 发明申请

US20170083766A1 DETECTED OBJECT TRACKER FOR A VIDEO ANALYTICS SYSTEM 审中-公开
公开(公告)号：US20170083766A1
公开(公告)日：2017-03-23
申请号：US14863311
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 , G06K9/62
CPC分类号： G06K9/6215 , G06K9/00771 , G06K2009/3291 , G06T7/11 , G06T7/194 , G06T7/246 , G06T7/254 , G06T7/277 , G06T2207/10024 , 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.

40. 发明申请

US20170083764A1 DETECTED OBJECT TRACKER FOR A VIDEO ANALYTICS SYSTEM 审中-公开
公开(公告)号：US20170083764A1
公开(公告)日：2017-03-23
申请号：US14863267
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 , G06K9/62
CPC分类号： G06T7/251 , G06T7/11 , G06T7/194 , 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.

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