公开(公告)号：WO2013087711A2

公开(公告)日：2013-06-20

申请号：PCT/EP2012075259

申请日：2012-12-12

Applicant: ATS GROUP IP HOLDINGS LTD ,  HARTMANN MELANIE ,  BAUER ALEXANDER

Inventor： HARTMANN MELANIE ,  BAUER ALEXANDER ,  BLANKE ULF

IPC: H04Q9/00

CPC classification number: H04Q9/00 ,  H04Q2209/75

Abstract: A computer implemented method, computer program product and computer system for sensor classification. The computer system receives from an unclassified sensor node a series of measurement data. The unclassified sensor node is unknown in an infrastructure knowledge base. The computer system logs the series of measurement data in a first database. A test pattern is extracted from the series of measurement data. The computer system determines a similarity measure of for the test pattern and a predefined reference pattern stored in a second database. The reference pattern classifies a capability type. The similarity measure is associated with a confidence value. The computer system suggests the capability type for classifying the unclassified sensor node if the confidence value is above a predefined threshold value and initiates an update of the infrastructure knowledge base to register the previously unclassified sensor node with the suggested capability type if the suggested capability type is accepted.

Abstract translation: 用于传感器分类的计算机实现的方法，计算机程序产品和计算机系统 计算机系统从未分类的传感器节点接收一系列测量数据。 未分类的传感器节点在基础设施知识库中是未知的。 计算机系统将一系列测量数据记录在第一数据库中。 从一系列测量数据中提取测试图案。 计算机系统确定测试模式和存储在第二数据库中的预定参考模式的相似性度量。 参考模式将能力类型分类。 相似性度量与一个置信度值相关联。 计算机系统建议如果置信度值高于预定阈值则对未分类的传感器节点进行分类的能力类型，并且如果建议的能力类型是建议的能力类型，则启动基础设施知识库的更新以将先前未分类的传感器节点注册为建议的能力类型 公认。

公开(公告)号：WO2014013012A1

公开(公告)日：2014-01-23

申请号：PCT/EP2013065174

申请日：2013-07-18

Applicant: ATS GROUP IP HOLDINGS LTD ,  AGT GROUP R & D GMBH

Inventor： SCHLATTMANN MARKUS ,  HAMER HENNING ,  BLANKE ULF

IPC: G06T7/20

CPC classification number: G06K9/00624 ,  G06T7/20 ,  G06T2207/10016 ,  G06T2207/30196 ,  G06T2207/30232 ,  G06T2207/30241 ,  H04N7/18

Abstract: In queues, persons - or objects (120) in general - move inside an area (110) to a target (112), such as to a counter. The queue has movement characteristics in terms of speed, waiting times and queue form. A computer-implemented approach obtains the characteristics by receiving a sequence (140) of image frames (141, 142, 143, 49) that represent the surveillance area (110); calculating flow vectors that indicate an optical displacement for the sequence (140) of image frames (141/142, 142/143); extending one of the flow vectors as lead vector in extension directions; determining intermediate vectors by using flow vectors along the extension directions; and selecting one the intermediate vectors as the new lead vector. The steps are repeated to concatenate the lead vectors to the movement characteristics (190).

Abstract translation: 在队列中，人或物体（120）一般在区域（110）内移动到目标（112），例如到柜台。 队列在速度，等待时间和队列形式方面具有运动特征。 计算机实现的方法通过接收表示监视区域（110）的图像帧（141,142,143,49）的序列（140）来获得特征; 计算指示图像帧（141 / 142,142 / 143）的序列（140）的光学位移的流向量; 将一个流向量作为引导向量在延伸方向上延伸; 通过使用沿着延伸方向的流向量来确定中间向量; 并选择一个中间矢量作为新的前导矢量。 重复这些步骤以将引导向量连接到运动特征（190）。