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    • 74. 发明授权
    • Radar guided vision system for vehicle validation and vehicle motion characterization
    • 雷达导航视觉系统用于车辆验证和车辆运动特性
    • US08355539B2
    • 2013-01-15
    • US12146897
    • 2008-06-26
    • Yi TanFeng HanJayan EledathRakesh KumarFaroog Abdel-kareem Ibrahim
    • Yi TanFeng HanJayan EledathRakesh KumarFaroog Abdel-kareem Ibrahim
    • G06K9/00
    • G06K9/00798
    • A method for determining whether a target vehicle in front of a host vehicle intends to change lanes using radar data and image data is disclosed, comprising the steps of processing the image data to detect the boundaries of the lane of the host vehicle; estimating a ground plane by determining a projected vanishing point of the detected lane boundaries; using a camera projection matrix to map the target vehicle from the radar data to image coordinates; and determining lane change intentions of the target vehicle based on a moving trajectory and an appearance change of the target vehicle. Determining lane change intentions based on a moving trajectory of the target vehicle is based on vehicle motion trajectory relative to the center of the lane such that the relative distance of the target vehicle from the center of the lane follows a predetermined trend. Determining lane change intentions based on an appearance change of the target vehicle is based on a template that tracks changes to the appearance of the rear part of the target vehicle due to rotation.
    • 公开了一种用于确定本车辆前方的目标车辆是否使用雷达数据和图像数据来改变车道的方法,包括以下步骤:处理图像数据以检测主车辆的车道边界; 通过确定检测到的车道边界的预计消失点来估计接地平面; 使用相机投影矩阵将目标车辆从雷达数据映射到图像坐标; 以及基于所述目标车辆的移动轨迹和外观变化来确定所述目标车辆的车道改变意图。 基于目标车辆的移动轨迹确定车道改变意图是基于相对于车道中心的车辆运动轨迹,使得目标车辆与车道中心的相对距离遵循预定趋势。 基于目标车辆的外观变化确定车道改变意图是基于跟踪由于旋转而导致的目标车辆的后部的外观的变化的模板。
    • 77. 发明授权
    • VPLS N-PE redundancy using pseudo wire fast failover
    • VPLS N-PE冗余使用伪线快速故障切换
    • US08107386B2
    • 2012-01-31
    • US12027725
    • 2008-02-07
    • Rakesh KumarJay ShahJason Xiaoguang Chen
    • Rakesh KumarJay ShahJason Xiaoguang Chen
    • H04J1/16
    • H04L45/22H04L43/0811H04L45/04H04L45/28H04L45/68
    • 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.
    • 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。
    • 78. 发明申请
    • System and method for detection of multi-view/multi-pose objects
    • 用于检测多视点/多姿态对象的系统和方法
    • US20120002869A1
    • 2012-01-05
    • US13134885
    • 2011-06-20
    • Feng HanYing ShanHarpreet Singh SawhneyRakesh Kumar
    • Feng HanYing ShanHarpreet Singh SawhneyRakesh Kumar
    • G06K9/62
    • G06K9/6256
    • The present invention provides a computer implemented process for detecting multi-view multi-pose objects. The process comprises training of a classifier for each intra-class exemplar, training of a strong classifier and combining the individual exemplar-based classifiers with a single objective function. This function is optimized using the two nested AdaBoost loops. The first loop is the outer loop that selects discriminative candidate exemplars. The second loop, the inner loop selects the discriminative candidate features on the selected exemplars to compute all weak classifiers for a specific position such as a view/pose. Then all the computed weak classifiers are automatically combined into a final classifier (strong classifier) which is the object to be detected.
    • 本发明提供了一种用于检测多视点多姿态对象的计算机实现过程。 该过程包括针对每个类内样本的分类器的训练,强分类器的训练和将单个基于样本的分类器与单个目标函数组合。 使用两个嵌套的AdaBoost循环来优化此功能。 第一个循环是选择区分候选样本的外循环。 第二个循环,内循环选择所选样本上的鉴别候选特征,以计算特定位置(例如视图/姿态)的所有弱分类器。 然后将所有计算的弱分类器自动组合成最终分类器(强分类器),该分类器是要检测的对象。