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    VPLS N-PE REDUNDANCY USING PSEUDO WIRE FAST FAILOVER
    21.
    发明申请
    VPLS N-PE REDUNDANCY USING PSEUDO WIRE FAST FAILOVER 有权
    VPLS N-PE REDUNDANCY使用PSEUDO线快速故障

    公开(公告)号：US20090201831A1

    公开(公告)日：2009-08-13

    申请号：US12027725

    申请日：2008-02-07

    Applicant: Rakesh Kumar ,  Jay Shah ,  Jason Xiaoguang Chen

    Inventor： Rakesh Kumar ,  Jay Shah ,  Jason Xiaoguang Chen

    IPC: H04L12/28

    CPC classification number: H04L45/22 ,  H04L43/0811 ,  H04L45/04 ,  H04L45/28 ,  H04L45/68

    Abstract: In one example embodiment, a system and method is provided that includes establishing a plurality of Pseudo Wire (PW) connections between a first network appliance region and a second network appliance region to transmit data from the first network appliance region to the second network appliance region along an active PW. Further, the method includes disabling the active PW when a failure of the active PW is detected. Additionally, the method may include selecting an inactive PW to become a new active PW such that the data may be transmitted from the first network appliance region to the second network appliance region. Moreover, the method includes switching from the active PW to the new active PW.

    Abstract translation: 在一个示例实施例中，提供了一种系统和方法，其包括在第一网络设备区域和第二网络设备区域之间建立多条伪线（PW）连接，以将数据从第一网络设备区域传输到第二网络设备区域 沿着主动PW。 此外，该方法包括当检测到活动PW的故障时禁用活动PW。 此外，该方法可以包括选择不活动的PW以成为新的活动PW，使得数据可以从第一网络设备区域发送到第二网络设备区域。 此外，该方法包括从主动PW切换到新的主动PW。

    Moving Target Detection in the Presence of Parallax
    24.
    发明申请
    Moving Target Detection in the Presence of Parallax 有权
    在视差存在下移动目标检测

    公开(公告)号：US20080089556A1

    公开(公告)日：2008-04-17

    申请号：US11763559

    申请日：2007-06-15

    Applicant: Garbis Salgian ,  Supun Samarasekera ,  Jiangjian Xiao ,  James Bergen ,  Rakesh Kumar ,  Feng Han

    Inventor： Garbis Salgian ,  Supun Samarasekera ,  Jiangjian Xiao ,  James Bergen ,  Rakesh Kumar ,  Feng Han

    IPC: G06K9/00

    CPC classification number: G06K9/3241 ,  G06K9/209 ,  G06T7/285 ,  G06T2207/10021 ,  G06T2207/30212 ,  G06T2207/30252

    Abstract: A method for detecting a moving target is disclosed that receives a plurality of images from at least one camera; receives a measurement of scale from one of a measurement device and a second camera; calculates the pose of the at least one camera over time based on the plurality of images and the measurement of scale; selects a reference image and an inspection image from the plurality of images of the at least one camera; and detects a moving target from the reference image and the inspection image based on the orientation of corresponding portions in the reference image and the inspection image relative to a location of an epipolar direction common to the reference image and the inspection image; and displays any detected moving target on a display. The measurement of scale can derived from a second camera or, for example, a wheel odometer. The method can also detect moving targets by combining the above epipolar method with a method based on changes in depth between the inspection image and the reference image and based on changes in flow between the inspection image and the reference image.

    Abstract translation: 公开了一种用于检测移动目标的方法，其从至少一个相机接收多个图像; 从测量装置和第二相机中的一个接收刻度的测量; 基于多个图像和尺度的测量来计算随时间的至少一个相机的姿态; 从所述至少一个照相机的多个图像中选择参考图像和检查图像; 并且基于所述参考图像和所述检查图像中的对应部分相对于所述参考图像和所述检查图像共同的核极方向的位置的取向，从所述参考图像和所述检查图像中检测移动目标; 并在显示器上显示任何检测到的移动目标。 刻度的测量可以从第二相机或例如车轮里程表得到。 该方法还可以通过将上述对极方法与基于检查图像和参考图像之间的深度变化的方法并基于检查图像与参考图像之间的流动变化组合来检测移动目标。

    Flexible layer tracking with weak online appearance model
    26.
    发明申请
    Flexible layer tracking with weak online appearance model 有权
    柔性层跟踪与弱在线外观模型

    公开(公告)号：US20070086621A1

    公开(公告)日：2007-04-19

    申请号：US11250181

    申请日：2005-10-13

    Applicant: Manoj Aggarwal ,  Harpreet Sawhney ,  Rakesh Kumar ,  Supun Samarasekera

    Inventor： Manoj Aggarwal ,  Harpreet Sawhney ,  Rakesh Kumar ,  Supun Samarasekera

    IPC: G06K9/00

    CPC classification number: G06K9/32 ,  G06K9/00369 ,  G06T7/251 ,  G06T7/277 ,  G06T2207/10016 ,  G06T2207/30196 ,  G06T2207/30232

    Abstract: Method for tracking an object recorded within a selected frame of a sequence of frames of video data, using a plurality of layers, where at least one object layer of the plurality of layers represents the object includes initializing layer ownership probabilities for pixels of the selected frame using a non-parametric motion model, estimating a set of motion parameters of the plurality of layers for the selected frame using a parametric maximization algorithm and tracking the object. The non-parametric motion model is optical flow and includes warping the mixing probabilities, the appearances of the plurality of layers, and the observed pixel data from the pixels of the preceding frame to the pixels of the selected frame to initialize the layer ownership probabilities for the pixels of the selected frame.

    Abstract translation: 用于使用多个层来跟踪记录在视频数据帧序列的所选帧内的对象的方法，其中多个层中的至少一个对象层表示对象包括初始化所选帧的像素的层所有权概率 使用非参数运动模型，使用参数最大化算法估计所选择的帧的所述多个层的运动参数的集合并跟踪所述对象。 非参数运动模型是光流，包括混合概率，多层的出现以及从前一帧的像素到所选帧的像素的观察像素数据的变形，以初始化层所有权概率 所选帧的像素。

    Method and apparatus for unsupervised learning of discriminative edge measures for vehicle matching between non-overlapping cameras
    29.
    发明申请
    Method and apparatus for unsupervised learning of discriminative edge measures for vehicle matching between non-overlapping cameras 有权
    用于非重叠摄像机之间的车辆匹配的鉴别边缘测量的无监督学习的方法和装置

    公开(公告)号：US20060165277A1

    公开(公告)日：2006-07-27

    申请号：US11295143

    申请日：2005-12-05

    Applicant: Ying Shan ,  Rakesh Kumar ,  Harpreet Sawhney

    Inventor： Ying Shan ,  Rakesh Kumar ,  Harpreet Sawhney

    IPC: G06K9/62 ,  G06K9/00

    CPC classification number: G06K9/6293 ,  G06K9/00785 ,  G06K9/3241 ,  G06K2209/23 ,  G06T7/33

    Abstract: A method and apparatus for unsupervised learning of measures for matching objects between images from at least two non-overlapping cameras is disclosed The method includes collecting at least one two pairs of feature maps, where the at least one two pairs of feature maps are derived from features of objects captured in the images. The method further includes computing, as a function of at least one two pairs of feature maps, at least one first and second match measures, wherein the first match measure is of a same class and the second match measure is of a different class.

    Abstract translation: 公开了一种用于无人监督学习用于在来自至少两个非重叠相机的图像之间匹配对象的措施的方法和装置。该方法包括收集至少一对两对特征图，其中至少一对两对特征图从 在图像中捕获的对象的特征。 所述方法还包括根据至少一对两对特征图来计算至少一个第一和第二匹配度量，其中所述第一匹配度量具有相同类别，并且所述第二匹配度量是不同类别的。

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