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    • 4. 发明申请
    • SYSTEM ARCHITECTURE AND PROCESS FOR SEAMLESS ADAPTATION TO CONTEXT AWARE BEHAVIOR MODELS
    • 系统架构和无缝适应过程以突出特征行为模型
    • US20090210373A1
    • 2009-08-20
    • US12034164
    • 2008-02-20
    • Juan YuHasan Timucin OzdemirKuo Chu Lee
    • Juan YuHasan Timucin OzdemirKuo Chu Lee
    • G06N5/02
    • G06K9/00771A61B5/1128A61B5/16A61B5/7267G06K9/00785
    • A surveillance system implements an architecture and process to support real-time abnormal behavior assessment operations in a distributed scalable sensor network. An automated behavior model builder generates behavior models from sensor data. A plurality of abnormal behavior scoring engines operating concurrently to generate abnormal behavior assessment models by scoring the behavior models. An execution performance manager performs fast switching of behavior models for the abnormal behavior scoring engines. The execution performance manager performs detection of abnormal behavior score distribution characteristic deviation by comparing a current abnormal behavior assessment model to a pre-recorded abnormal behavior assessment model. The execution performance manager selects a pre-recorded behavior model for the abnormal behavior scoring engines when the deviation exceeds a predetermined threshold.
    • 监控系统实现一种架构和过程,以支持分布式可扩展传感器网络中的实时异常行为评估操作。 自动行为模型构建器从传感器数据生成行为模型。 多个异常行为评分引擎同时运行,通过评分行为模型来产生异常行为评估模型。 执行性能管理员可以快速切换异常行为评分引擎的行为模型。 执行绩效管理者通过将当前的异常行为评估模型与预先记录的异常行为评估模型进行比较来执行异常行为评分分布特征偏差的检测。 当偏差超过预定阈值时,执行性能管理器为异常行为评分引擎选择预先记录的行为模型。
    • 6. 发明申请
    • THREAT-DETECTION IN A DISTRIBUTED MULTI-CAMERA SURVEILLANCE SYSTEM
    • 分布式多摄像机监控系统中的故障检测
    • US20080198231A1
    • 2008-08-21
    • US11675849
    • 2007-02-16
    • Hasan Timucin OZDEMIRKuo Chu Lee
    • Hasan Timucin OZDEMIRKuo Chu Lee
    • H04N7/18
    • G08B13/19608G08B13/19645G08B25/009H04N7/188
    • A method is provided for detecting a threat in a distributed multi-camera surveillance system. The method includes: monitoring movement of an object in a field of view of a first camera using software installed at the first camera; detecting a suspicious object at the first camera when movement of the object does not conform with a motion flow model residing at the first camera; sending a tracking request from the first camera to a second camera upon detecting the suspicious object at the first camera; monitoring movement of the object in a field of view of the second camera using software installed at the second camera; assigning threat scores at the second camera when the movement of the object does not conform with a motion flow model residing at the second camera; and generating an alarm based in part on the threat scores detected at the first camera and the second camera.
    • 提供了一种用于检测分布式多摄像机监视系统中的威胁的方法。 该方法包括:使用安装在第一相机的软件监视第一相机的视场中的对象的移动; 当物体的移动不符合驻留在第一相机的运动流模型时,在第一相机处检测可疑物体; 在第一相机检测到可疑对象时,将第一相机的跟踪请求发送到第二相机; 使用安装在第二相机上的软件监视第二相机的视场中的对象的移动; 当对象的移动不符合驻留在第二相机的运动流模型时,在第二相机处分配威胁分数; 并且部分地基于在第一相机和第二相机处检测到的威胁分数来产生报警。
    • 9. 发明申请
    • Data Mining Method and System For Estimating Relative 3D Velocity and Acceleration Projection Functions Based on 2D Motions
    • 基于二维运动估计相对三维速度和加速度投影函数的数据挖掘方法与系统
    • US20110205355A1
    • 2011-08-25
    • US12709046
    • 2010-02-19
    • Lipin LIUKuo Chu Lee
    • Lipin LIUKuo Chu Lee
    • H04N7/18
    • G06T7/80G06T2207/10016G06T2207/30232
    • A method for determining a transformation matrix used to transform data from a first image of a space to a second image of the space is disclosed. The method comprises receiving image data from a video camera monitoring the space, wherein the video camera generates image data of an object moving through the space and determining spatio-temporal locations of the object with respect to a field of view of the camera from the image data. The method further comprises determining observed attributes of motion of the object in relation to the field of view of the camera based on the spatio-temporal locations of the object, the observed attributes including at least one of a velocity of the object with respect to the field of view of the camera and an acceleration of the object with respect to the field of view of the camera. The method also includes determining the transformation matrix based on the observed attributes of the motion of the object.
    • 公开了一种用于确定用于将数据从空间的第一图像转换成空间的第二图像的变换矩阵的方法。 该方法包括从监视该空间的视频摄像机接收图像数据,其中摄像机生成通过空间移动的物体的图像数据,并根据图像确定对象相对于照相机的视场的时空位置 数据。 该方法还包括基于对象的时空位置来确定对象相对于摄像机的视场的运动的观察属性,观察到的属性包括对象相对于对象的速度的至少一个 摄像机的视野以及相对于摄像机的视场的对象的加速度。 该方法还包括基于观察到的物体的运动的属性来确定变换矩阵。
    • 10. 发明授权
    • System architecture and process for seamless adaptation to context aware behavior models
    • 系统架构和过程,用于无缝适应上下文感知行为模型
    • US07962435B2
    • 2011-06-14
    • US12034164
    • 2008-02-20
    • Juan YuHasan Timucin OzdemirKuo Chu Lee
    • Juan YuHasan Timucin OzdemirKuo Chu Lee
    • G06F17/00G06N5/02G08B13/00
    • G06K9/00771A61B5/1128A61B5/16A61B5/7267G06K9/00785
    • A surveillance system implements an architecture and process to support real-time abnormal behavior assessment operations in a distributed scalable sensor network. An automated behavior model builder generates behavior models from sensor data. A plurality of abnormal behavior scoring engines operating concurrently to generate abnormal behavior assessment models by scoring the behavior models. An execution performance manager performs fast switching of behavior models for the abnormal behavior scoring engines. The execution performance manager performs detection of abnormal behavior score distribution characteristic deviation by comparing a current abnormal behavior assessment model to a pre-recorded abnormal behavior assessment model. The execution performance manager selects a pre-recorded behavior model for the abnormal behavior scoring engines when the deviation exceeds a predetermined threshold.
    • 监控系统实现一种架构和过程,以支持分布式可扩展传感器网络中的实时异常行为评估操作。 自动行为模型构建器从传感器数据生成行为模型。 多个异常行为评分引擎同时运行,通过评分行为模型来产生异常行为评估模型。 执行性能管理员可以快速切换异常行为评分引擎的行为模型。 执行绩效管理者通过将当前的异常行为评估模型与预先记录的异常行为评估模型进行比较来执行异常行为评分分布特征偏差的检测。 当偏差超过预定阈值时,执行性能管理器为异常行为评分引擎选择预先记录的行为模型。