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公开(公告)号：US09396621B2
公开(公告)日：2016-07-19
申请号：US13428982
申请日：2012-03-23
CPC分类号： G06K9/00771 , G06K7/10009 , G06K9/00335 , G06K9/6256 , G06K9/6269 , G07G1/0036 , G07G1/0045 , G07G1/009 , H04N7/18 , H04N7/183
摘要： Embodiments of the present invention provide a system, method, and program product to determine whether a product has been successfully purchased by identifying in a video record when a movement of a product adjacent to a scanner occurs, and whether the scanner did not record a purchase transaction at that time; measuring a difference in time between the time of the movement of the product and a time of another movement of a product, and determining by a trained support vector machine a likelihood that the product was successfully purchased. Alternately, the difference in time can be measured between the time of the movement of the product and a time of a transaction record, or between the time of the movement of the product and a boundary time. The support vector machine can use a radial basis function kernel and can generate a decision value and a confidence score.
摘要翻译： 本发明的实施例提供一种系统，方法和程序产品，用于通过在与扫描仪相邻的产品的移动发生时识别产品在视频记录中是否成功购买，以及扫描仪是否没有记录购买 当时交易; 测量产品移动时间与产品的另一次移动时间之间的时间差，以及由经过训练的支持向量机确定产品被成功购买的可能性。 或者，可以在产品移动时间与交易记录时间之间，或在产品移动时间与边界时间之间测量时间差。 支持向量机可以使用径向基函数内核，并可以生成决策值和置信度分数。
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公开(公告)号：US08682032B2
公开(公告)日：2014-03-25
申请号：US13213262
申请日：2011-08-19
申请人： Quanfu Fan , Prasad Gabbur , Sachiko Miyazawa , Jiyan Pan , Sharathchandra U. Pankanti , Hoang Trinh
发明人： Quanfu Fan , Prasad Gabbur , Sachiko Miyazawa , Jiyan Pan , Sharathchandra U. Pankanti , Hoang Trinh
IPC分类号： G06K9/62
CPC分类号： G06K9/6292 , G06K9/6885
摘要： Events are classified through string pattern recognition. Text labels are assigned to image primitives in a time-ordered set of training images and to related time-ordered transactions in an associated training transaction log in a combined time-ordered training string of text labels as a function of image types. Transactions are labeled in a training transaction log with a transaction label, a training primitive image of a start of a transaction with a start image text label, a training primitive of an entry of a transaction into the log with an entry image text label, and a training primitive of a conclusion of a transaction with an ending image text label. Positive subset string patterns are discovered representing true events from the combined time-ordered training string of text labels, and negative subset string patterns defined by removing single transaction primitive labels from the positive subset string patterns.
摘要翻译： 事件通过字符串模式识别分类。 将文本标签分配给训练图像的时间有序集合中的图像原语以及相关联的训练事务日志中的相关时间顺序事务，其中文本标签的组合时间有序训练串作为图像类型的函数。 事务在具有事务标签的训练事务日志中被标记，具有开始图像文本标签的事务开始的训练原始图像，具有条目图像文本标签的事务入口的训练原语以及 与结束图像文本标签交易结论的训练原语。 从组合的时间有序的文本标签的训练串中发现表示真实事件的正的子集字符串模式，以及通过从正的子集字符串模式中去除单个事务原始标签而定义的负子集串模式。
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公开(公告)号：US20130044942A1
公开(公告)日：2013-02-21
申请号：US13213262
申请日：2011-08-19
申请人： Quanfu Fan , Prasad Gabbur , Sachiko Miyazawa , Jiyan Pan , Sharathchandra U. Pankanti , Hoang Trinh
发明人： Quanfu Fan , Prasad Gabbur , Sachiko Miyazawa , Jiyan Pan , Sharathchandra U. Pankanti , Hoang Trinh
IPC分类号： G06K9/62
CPC分类号： G06K9/6292 , G06K9/6885
摘要： Events are classified through string pattern recognition. Text labels are assigned to image primitives in a time-ordered set of training images and to related time-ordered transactions in an associated training transaction log in a combined time-ordered training string of text labels as a function of image types. Transactions are labeled in a training transaction log with a transaction label, a training primitive image of a start of a transaction with a start image text label, a training primitive of an entry of a transaction into the log with an entry image text label, and a training primitive of a conclusion of a transaction with an ending image text label. Positive subset string patterns are discovered representing true events from the combined time-ordered training string of text labels, and negative subset string patterns defined by removing single transaction primitive labels from the positive subset string patterns.
摘要翻译： 事件通过字符串模式识别分类。 将文本标签分配给训练图像的时间有序集合中的图像原语以及相关联的训练事务日志中的相关时间顺序事务，其中文本标签的组合时间有序训练串作为图像类型的函数。 事务在具有事务标签的训练事务日志中被标记，具有开始图像文本标签的事务开始的训练原始图像，具有条目图像文本标签的事务入口的训练原语以及 与结束图像文本标签交易结论的训练原语。 从组合的时间有序的文本标签的训练串中发现表示真实事件的正的子集字符串模式，以及通过从正的子集字符串模式中去除单个事务原始标签而定义的负子集串模式。
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4.发明申请公开(公告)号：US20130250115A1
公开(公告)日：2013-09-26
申请号：US13428982
申请日：2012-03-23
CPC分类号： G06K9/00771 , G06K7/10009 , G06K9/00335 , G06K9/6256 , G06K9/6269 , G07G1/0036 , G07G1/0045 , G07G1/009 , H04N7/18 , H04N7/183
摘要： Embodiments of the present invention provide a system, method, and program product to determine whether a product has been successfully purchased by identifying in a video record when a movement of a product adjacent to a scanner occurs, and whether the scanner did not record a purchase transaction at that time; measuring a difference in time between the time of the movement of the product and a time of another movement of a product, and determining by a trained support vector machine a likelihood that the product was successfully purchased. Alternately, the difference in time can be measured between the time of the movement of the product and a time of a transaction record, or between the time of the movement of the product and a boundary time. The support vector machine can use a radial basis function kernel and can generate a decision value and a confidence score.
摘要翻译： 本发明的实施例提供一种系统，方法和程序产品，用于通过在与扫描仪相邻的产品的移动发生时识别产品在视频记录中是否成功购买，以及扫描仪是否没有记录购买 当时交易; 测量产品移动时间与产品的另一次移动时间之间的时间差，以及由经过训练的支持向量机确定产品被成功购买的可能性。 或者，可以在产品移动时间与交易记录时间之间，或在产品移动时间与边界时间之间测量时间差。 支持向量机可以使用径向基函数内核，并可以生成决策值和置信度分数。
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公开(公告)号：US20130050517A1
公开(公告)日：2013-02-28
申请号：US13218845
申请日：2011-08-26
CPC分类号： G06K9/00 , G06K9/00771 , H04N5/232 , H04N5/23222
摘要： Visual content in images captured from a scene by a camera in each of a plurality of different pose settings are analyzed to determine predicted occurrences of a transaction associated with the visual content in each pose, which are compared with actual transaction occurrence data to generate performance values for each pose as a function difference between the predicted and actual transactions. Optimized poses are chosen having the best performance value, wherein a camera controller may place the camera in the optimum pose for use in monitoring the scene and generating the primitives of interest associated with the transactions.
摘要翻译： 分析由多个不同姿势设置中的每一个中的摄像机从场景捕获的图像中的视觉内容，以确定与每个姿态中的视觉内容相关联的事务的预测出现，其与实际交易发生数据进行比较以生成性能值 对于每个姿势作为预测和实际交易之间的功能差异。 选择具有最佳性能值的优化姿态，其中相机控制器可将相机放置在用于监视场景的最佳姿态并产生与交易相关联的感兴趣的原始图案。
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公开(公告)号：US08582803B2
公开(公告)日：2013-11-12
申请号：US12905272
申请日：2010-10-15
申请人： Lei Ding , Quanfu Fan , Prasad Gabbur , Arun Hampapur , Sachiko Miyazawa , Sharathchandra U. Pankanti
发明人： Lei Ding , Quanfu Fan , Prasad Gabbur , Arun Hampapur , Sachiko Miyazawa , Sharathchandra U. Pankanti
IPC分类号： G06K9/00
CPC分类号： G06K9/6215 , G06K9/00335 , G06K9/00771
摘要： Determination of human behavior from an alignment of data streams includes acquiring visual image primitives from a video input comprising visual information relevant to a human activity. The primitives are temporally aligned to an optimally hypothesized sequence of primitives transformed from a sequence of transactions as a function of a distance metric between the observed primitive sequence and the transformed primitive sequence. More particularly, transforming includes comparing the distance metric costs and choosing and performing the lowest cost of temporally matching the observed primitives to one or more transactions, deleting a primitive, or associating a primitive with a pseudo transaction marker. Accordingly, alerts are issued based on analysis of the transformation of primitives.
摘要翻译： 从数据流的对齐确定人类行为包括从包括与人类活动相关的视觉信息的视频输入中获取视觉图像原语。 原语在时间上与从事件序列变换的最优假设序列的原语对齐，作为观察到的原始序列与经变换的原始序列之间的距离度量的函数。 更具体地，变换包括比较距离度量成本并选择和执行将观察到的原语暂时匹配到一个或多个事务，删除原语或将原语与伪交易标记相关联的最低成本。 因此，基于对原语的转换的分析发出警报。
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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
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。
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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
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。
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公开(公告)号：US20110280547A1
公开(公告)日：2011-11-17
申请号：US12986233
申请日：2011-01-07
IPC分类号： H04N5/91
CPC分类号： G06K9/00355 , G06T7/20 , G08B13/196
摘要： Video analytics data is audited through review of selective visual essence subsets of visual images from a visual image stream as a function of a temporal relationship of the essence subset images to a triggering alert event. The visual essence subset comprehends an image contemporaneous with the triggering alert event and one or more other images occurring before or after the contemporaneous image. The generated visual essence is presented for review to determine whether the triggering alert event is a true or false alert, or whether additional data from the visual image stream is required to make such a determination. If determined from the presented visual essence that the additional data is required make the true or false determination, then additional data is presented from the visual image stream for review.
摘要翻译： 通过从可视图像流中检查视觉图像的选择性视觉实质子集作为本质子集图像与触发警报事件的时间关系的函数来审查视频分析数据。 视觉本质子集包括与触发警报事件同时发生的图像以及在同期图像之前或之后发生的一个或多个其它图像。 生成的视觉精华被呈现供审查以确定触发警报事件是真实还是假警报，或者是否需要来自视觉图像流的附加数据进行这样的确定。 如果从呈现的视觉实质确定需要附加数据进行真实或错误的确定，则从视觉图像流中呈现附加数据以供审查。
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公开(公告)号：US20120093370A1
公开(公告)日：2012-04-19
申请号：US12905272
申请日：2010-10-15
申请人： Lei Ding , Quanfu Fan , Prasad Gabbur , Arun Hampapur , Sachiko Miyazawa , Sharathchandra U. Pankanti
发明人： Lei Ding , Quanfu Fan , Prasad Gabbur , Arun Hampapur , Sachiko Miyazawa , Sharathchandra U. Pankanti
IPC分类号： G06K9/00
CPC分类号： G06K9/6215 , G06K9/00335 , G06K9/00771
摘要： Determination of human behavior from an alignment of data streams includes acquiring visual image primitives from a video input comprising visual information relevant to a human activity. The primitives are temporally aligned to an optimally hypothesized sequence of primitives transformed from a sequence of transactions as a function of a distance metric between the observed primitive sequence and the transformed primitive sequence. More particularly, transforming includes comparing the distance metric costs and choosing and performing the lowest cost of temporally matching the observed primitives to one or more transactions, deleting a primitive, or associating a primitive with a pseudo transaction marker. Accordingly, alerts are issued based on analysis of the transformation of primitives.
摘要翻译： 从数据流的对齐确定人类行为包括从包括与人类活动相关的视觉信息的视频输入中获取视觉图像原语。 原语在时间上与从事件序列变换的最优假设序列的原语对齐，作为观察到的原始序列与经变换的原始序列之间的距离度量的函数。 更具体地，变换包括比较距离度量成本并选择和执行将观察到的原语暂时匹配到一个或多个事务，删除原语或将原语与伪交易标记相关联的最低成本。 因此，基于对原语的转换的分析发出警报。
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