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1. 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。

2. 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。

3. 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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