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    DYNAMICALLY LEARNING ATTRIBUTES OF A POINT OF SALE OPERATOR
    1.
    发明申请
    DYNAMICALLY LEARNING ATTRIBUTES OF A POINT OF SALE OPERATOR 失效
    动态点销售操作员的动态学习属性

    公开(公告)号：US20100111404A1

    公开(公告)日：2010-05-06

    申请号：US12261304

    申请日：2008-10-30

    申请人： Russell P. Bobbitt ,  Quanfu Fan ,  Arun Hampapur ,  Frederik C. M. Kjeldsen ,  Sharathchandra U. Pankanti ,  Akira Yanagawa ,  Yun Zhai

    发明人： Russell P. Bobbitt ,  Quanfu Fan ,  Arun Hampapur ,  Frederik C. M. Kjeldsen ,  Sharathchandra U. Pankanti ,  Akira Yanagawa ,  Yun Zhai

    IPC分类号： G06K9/66

    CPC分类号： G06K9/00771

    摘要： An approach that dynamically learns a set of attributes of an operator of a point of sale (POS) is provided. In one embodiment, there is an attribute tool, including an extraction component configured to receive sensor data of a set of moving objects, and extract a set of attributes from each of the set of moving objects captured within the scan area at the POS; an identification component configured to update an appearance model with the set of attributes from each of the set of moving objects; and an analysis component configured to analyze the appearance model to identify at least one of the set of moving objects as an operator of the POS.

    摘要翻译： 提供了动态学习销售点（POS）的运营商的一组属性的方法。 在一个实施例中，存在一种属性工具，包括被配置为接收一组移动对象的传感器数据的提取部件，并且从POS中的扫描区域内捕获的移动对象集合中的每一个提取一组属性; 识别组件，被配置为使用来自所述一组移动对象的所述一组属性来更新外观模型; 以及分析部件，被配置为分析所述外观模型以将所述一组移动对象中的至少一个识别为所述POS的操作者。

    Dynamically learning attributes of a point of sale operator
    2.
    发明授权
    Dynamically learning attributes of a point of sale operator 失效
    动态学习销售点操作员的属性

    公开(公告)号：US08494214B2

    公开(公告)日：2013-07-23

    申请号：US12261304

    申请日：2008-10-30

    申请人： Russell P. Bobbitt ,  Quanfu Fan ,  Arun Hampapur ,  Frederik C. M. Kjeldsen ,  Sharathchandra U. Pankanti ,  Akira Yanagawa ,  Yun Zhai

    发明人： Russell P. Bobbitt ,  Quanfu Fan ,  Arun Hampapur ,  Frederik C. M. Kjeldsen ,  Sharathchandra U. Pankanti ,  Akira Yanagawa ,  Yun Zhai

    IPC分类号： G06K9/00

    CPC分类号： G06K9/00771

    摘要： An approach that dynamically learns a set of attributes of an operator of a point of sale (POS) is provided. In one embodiment, there is an attribute tool, including an extraction component configured to receive sensor data of a set of moving objects, and extract a set of attributes from each of the set of moving objects captured within the scan area at the POS; an identification component configured to update an appearance model with the set of attributes from each of the set of moving objects; and an analysis component configured to analyze the appearance model to identify at least one of the set of moving objects as an operator of the POS.

    摘要翻译： 提供了动态学习销售点（POS）的运营商的一组属性的方法。 在一个实施例中，存在一种属性工具，包括被配置为接收一组移动对象的传感器数据的提取部件，并且从POS中的扫描区域内捕获的移动对象集合中的每一个提取一组属性; 识别组件，被配置为使用来自所述一组移动对象的所述一组属性来更新外观模型; 以及分析部件，被配置为分析所述外观模型以将所述一组移动对象中的至少一个识别为所述POS的操作者。

    SYSTEM AND METHOD FOR AUTOMATICALLY DISTINGUISHING BETWEEN CUSTOMERS AND IN-STORE EMPLOYEES
    3.
    发明申请
    SYSTEM AND METHOD FOR AUTOMATICALLY DISTINGUISHING BETWEEN CUSTOMERS AND IN-STORE EMPLOYEES 失效
    客户和内部员工之间自动辨别的系统和方法

    公开(公告)号：US20100114802A1

    公开(公告)日：2010-05-06

    申请号：US12263928

    申请日：2008-11-03

    申请人： Russell P. Bobbitt ,  Quanfu Fan ,  Sharathchandra U. Pankanti ,  Akira Yanagawa ,  Yun Zhai

    发明人： Russell P. Bobbitt ,  Quanfu Fan ,  Sharathchandra U. Pankanti ,  Akira Yanagawa ,  Yun Zhai

    IPC分类号： G06F15/18 ,  G06N5/02

    CPC分类号： G06N99/005 ,  G06Q30/06

    摘要： An approach that automatically distinguishes between in-store customers and in-store employees is provided. In one embodiment, there is a learning tool configured to construct a model for an in-store employee; and a classifying tool, further comprising matching tool configured to: match attributes between a particular person and the constructed models for an in-store employee, the classifying tool configured to: classify persons into categories of employees and customers based on amount of matching attributes between a particular person and the model for an in-store employee.

    摘要翻译： 提供了一种自动区分店内客户和店内员工的方法。 在一个实施例中，存在用于构建店内员工的模型的学习工具; 以及分类工具，还包括匹配工具，其被配置为：匹配特定人员和所述店内员工的所构建的模型之间的属性，所述分类工具被配置为：基于所述员工和所述客户的类别， 一个特定的人和一个店内员工的模型。

    DETECTING POTENTIALLY FRAUDULENT TRANSACTIONS
    4.
    发明申请
    DETECTING POTENTIALLY FRAUDULENT TRANSACTIONS 审中-公开
    检测潜在的欺诈交易

    公开(公告)号：US20100114617A1

    公开(公告)日：2010-05-06

    申请号：US12261256

    申请日：2008-10-30

    申请人： Russell P. Bobbitt ,  Quanfu Fan ,  Sharathchandra U. Pankanti ,  Akira Yanagawa ,  Yun Zhai

    发明人： Russell P. Bobbitt ,  Quanfu Fan ,  Sharathchandra U. Pankanti ,  Akira Yanagawa ,  Yun Zhai

    IPC分类号： G08B31/00 ,  G06Q10/00

    CPC分类号： G08B13/19671 ,  G06Q20/202 ,  G06Q20/401 ,  G06Q30/06 ,  G06Q40/12

    摘要： An approach that detects potentially fraudulent transactions is provided. In one embodiment, there is a fraud detection tool including, an identification component configured to identify a first person present within a zone of interest at a point of sale (POS) device using a set of sensor devices; a transaction component configured to determine whether the POS device has performed a first transaction and a second transaction while the first person is present within the zone of interest at the POS device; an analysis component configured to: analyze a transaction type of the first transaction and the second transaction; and detect whether the second transaction is potentially fraudulent based on a determination of whether the POS device has performed a first transaction and a second transaction while the first person is within the zone of interest at the POS device, and an analysis of the transaction type of the second transaction.

    摘要翻译： 提供了一种检测潜在欺诈交易的方法。 在一个实施例中，存在欺诈检测工具，其包括识别组件，其被配置为使用一组传感器设备来识别在销售点（POS）设备处存在于感兴趣区域内的第一人物; 交易组件，被配置为当所述第一人在所述POS设备的所述感兴趣区域内存在时，确定所述POS设备是否已经执行了第一交易和第二交易; 分析组件，其被配置为：分析所述第一事务和所述第二事务的事务类型; 并且当所述第一人在所述POS设备的所述感兴趣区域内时，基于所述POS设备是否已经执行了第一交易和所述第二交易的确定来检测所述第二交易是否具有潜在的欺诈性，以及所述交易类型的分析 第二笔交易。

    System and method for automatically distinguishing between customers and in-store employees
    5.
    发明授权
    System and method for automatically distinguishing between customers and in-store employees 失效
    用于自动区分客户和店内员工的系统和方法

    公开(公告)号：US08694443B2

    公开(公告)日：2014-04-08

    申请号：US12263928

    申请日：2008-11-03

    申请人： Russell P. Bobbitt ,  Quanfu Fan ,  Sharathchandra U. Pankanti ,  Akira Yanagawa ,  Yun Zhai

    发明人： Russell P. Bobbitt ,  Quanfu Fan ,  Sharathchandra U. Pankanti ,  Akira Yanagawa ,  Yun Zhai

    IPC分类号： G06F15/18

    CPC分类号： G06N99/005 ,  G06Q30/06

    摘要： An approach that automatically distinguishes between in-store customers and in-store employees is provided. In one embodiment, there is a learning tool configured to construct a model for an in-store employee; a matching tool configured to match attributes between a particular person and the constructed models for an in-store employee; and a classifying tool configured to classify persons into categories of employees and customers based on amount of matching attributes between a particular person and the model for an in-store employee.

    摘要翻译： 提供了一种自动区分店内客户和店内员工的方法。 在一个实施例中，存在用于构建店内员工的模型的学习工具; 匹配工具，被配置为匹配特定人员和店内员工的构建模型之间的属性; 以及分类工具，其被配置为基于针对店内员工的特定人与模型之间的匹配属性的数量来将人员分类为雇员和顾客的类别。

    SEQUENTIAL EVENT DETECTION FROM VIDEO
    6.
    发明申请
    SEQUENTIAL EVENT DETECTION FROM VIDEO 有权
    视频中的顺序事件检测

    公开(公告)号：US20120008836A1

    公开(公告)日：2012-01-12

    申请号：US12834104

    申请日：2010-07-12

    申请人： Russell P. Bobbitt ,  Lei Ding ,  Quanfu Fan ,  Sachiko Miyazawa ,  Sharathchandra U. Pankanti ,  Yun Zhai

    发明人： Russell P. Bobbitt ,  Lei Ding ,  Quanfu Fan ,  Sachiko Miyazawa ,  Sharathchandra U. Pankanti ,  Yun Zhai

    IPC分类号： G06K9/00 ,  G06T11/20

    CPC分类号： G06K9/00335 ,  G06K9/00771 ,  G06K9/6224

    摘要： Human behavior is determined by sequential event detection by constructing a temporal-event graph with vertices representing adjacent first and second primitive images of a plurality of individual primitive images parsed from a video stream, and also of first and second idle states associated with the respective first and second primitive images. Constructing the graph is a function of an edge set between the adjacent first and second primitive images, and an edge weight set as a function of a discrepancy between computed visual features within regions of interest common to the adjacent first and second primitive images. A human activity event is determined as a function of a shortest distance path of the temporal-event graph vertices.

    摘要翻译： 通过构建具有表示从视频流解析的多个单独原始图像的相邻第一和第二原始图像的顶点的时间事件图以及与各自的第一和第二空闲状态相关联的第一和第二空闲状态，通过顺序事件检测来确定人的行为 和第二原始图像。 构造图是在相邻的第一和第二原始图像之间设置的边缘的函数，以及作为相邻第一和第二原始图像共同的感兴趣区域内的计算的视觉特征之间的差异的函数的边缘权重集合。 人类活动事件被确定为时间 - 事件图形顶点的最短距离路径的函数。

    Sequential event detection from video
    7.
    发明授权
    Sequential event detection from video 有权
    来自视频的连续事件检测

    公开(公告)号：US08548203B2

    公开(公告)日：2013-10-01

    申请号：US12834104

    申请日：2010-07-12

    申请人： Russell P. Bobbitt ,  Lei Ding ,  Quanfu Fan ,  Sachiko Miyazawa ,  Sharathchandra U. Pankanti ,  Yun Zhai

    发明人： Russell P. Bobbitt ,  Lei Ding ,  Quanfu Fan ,  Sachiko Miyazawa ,  Sharathchandra U. Pankanti ,  Yun Zhai

    IPC分类号： G06K9/00 ,  G06K9/48

    CPC分类号： G06K9/00335 ,  G06K9/00771 ,  G06K9/6224

    摘要： Human behavior is determined by sequential event detection by constructing a temporal-event graph with vertices representing adjacent first and second primitive images of a plurality of individual primitive images parsed from a video stream, and also of first and second idle states associated with the respective first and second primitive images. Constructing the graph is a function of an edge set between the adjacent first and second primitive images, and an edge weight set as a function of a discrepancy between computed visual features within regions of interest common to the adjacent first and second primitive images. A human activity event is determined as a function of a shortest distance path of the temporal-event graph vertices.

    摘要翻译： 通过构建具有表示从视频流解析的多个单独原始图像的相邻第一和第二原始图像的顶点的时间事件图以及与各自的第一和第二空闲状态相关联的第一和第二空闲状态，通过顺序事件检测来确定人的行为 和第二原始图像。 构造图是在相邻的第一和第二原始图像之间设置的边缘的函数，以及作为相邻第一和第二原始图像共同的感兴趣区域内的计算的视觉特征之间的差异的函数的边缘权重集合。 人类活动事件被确定为时间 - 事件图形顶点的最短距离路径的函数。