专利快速检索-快速检索全球专利，免费商用专利数据库-IPRDB

21. 发明授权

US08478048B2 Optimization of human activity determination from video 失效
标题翻译：从视频优化人类活动确定
公开(公告)号：US08478048B2
公开(公告)日：2013-07-02
申请号：US12832379
申请日：2010-07-08
申请人： Lei Ding , Quanfu Fan , Sharathchandra U. Pankanti
发明人： Lei Ding , Quanfu Fan , Sharathchandra U. Pankanti
IPC分类号： G06K9/46 , G06K9/00
CPC分类号： G06K9/00335 , G06K9/00771 , G08B13/19613
摘要： In an embodiment, automated analysis of video data for determination of human behavior includes providing a programmable device that segments a video stream into a plurality of discrete individual frame image primitives which are combined into a visual event that may encompass an activity of concern as a function of a hypothesis. The visual event is optimized by setting a binary variable to true or false as a function of one or more constraints. The optimized visual event is processed in view of associated non-video transaction data and the binary variable by associating the optimized visual event with a logged transaction if associable, issuing an alert if the binary variable is true and the optimized visual event is not associable with the logged transaction, and dropping the optimized visual event if the binary variable is false and the optimized visual event is not associable.
摘要翻译：在一个实施例中，用于确定人类行为的视频数据的自动分析包括提供将视频流分段成多个离散的单独帧图像原语的可编程设备，其被组合成视觉事件，视觉事件可以包含作为功能的关注活动一个假设。通过将二进制变量设置为true或false作为一个或多个约束的函数来优化视觉事件。考虑到相关联的非视频交易数据和二进制变量，通过将优化的可视事件与记录的事务相关联来处理优化的视觉事件，如果可关联，则如果二进制变量为真，并且优化的视觉事件不能与记录的事务，并且如果二进制变量为false并且优化的可视事件不可关联，则丢弃优化的可视事件。

22. 发明申请

US20120008819A1 OPTIMIZATION OF HUMAN ACTIVITY DETERMINATION FROM VIDEO 失效
标题翻译：从视频优化人类活动决定
公开(公告)号：US20120008819A1
公开(公告)日：2012-01-12
申请号：US12832379
申请日：2010-07-08
申请人： Lei Ding , Quanfu Fan , Sharathchandra U. Pankanti
发明人： Lei Ding , Quanfu Fan , Sharathchandra U. Pankanti
IPC分类号： G06K9/00
CPC分类号： G06K9/00335 , G06K9/00771 , G08B13/19613
摘要： In an embodiment, automated analysis of video data for determination of human behavior includes providing a programmable device that segments a video stream into a plurality of discrete individual frame image primitives which are combined into a visual event that may encompass an activity of concern as a function of a hypothesis. The visual event is optimized by setting a binary variable to true or false as a function of one or more constraints. The optimized visual event is processed in view of associated non-video transaction data and the binary variable by associating the optimized visual event with a logged transaction if associable, issuing an alert if the binary variable is true and the optimized visual event is not associable with the logged transaction, and dropping the optimized visual event if the binary variable is false and the optimized visual event is not associable.
摘要翻译：在一个实施例中，用于确定人类行为的视频数据的自动分析包括提供将视频流分段成多个离散的单独帧图像原语的可编程设备，其被组合成视觉事件，视觉事件可以包含作为功能的关注活动一个假设。通过将二进制变量设置为true或false作为一个或多个约束的函数来优化视觉事件。考虑到相关联的非视频交易数据和二进制变量，通过将优化的可视事件与记录的事务相关联来处理优化的视觉事件，如果可关联，则如果二进制变量为真，并且优化的视觉事件不能与记录的事务，并且如果二进制变量为false并且优化的可视事件不可关联，则丢弃优化的可视事件。

23. 发明授权

US08494214B2 Dynamically learning attributes of a point of sale operator 失效
标题翻译：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。

24. 发明授权

US09260122B2 Multisensor evidence integration and optimization in object inspection 有权
标题翻译：多传感器证据整合和物体检测优化
公开(公告)号：US09260122B2
公开(公告)日：2016-02-16
申请号：US13489489
申请日：2012-06-06
申请人： Norman Haas , Ying Li , Charles A. Otto , Sharathchandra U. Pankanti , Hoang Trinh
发明人： Norman Haas , Ying Li , Charles A. Otto , Sharathchandra U. Pankanti , Hoang Trinh
IPC分类号： B61L23/04 , G06T7/20
CPC分类号： B61L23/042
摘要： Video image data is acquired from synchronized cameras having overlapping views of objects moving past the cameras through a scene image in a linear array and with a determined speed. Processing units generate one or more object detections associated with confidence scores within frames of the camera video stream data. The confidence scores are modified as a function of constraint contexts including a cross-frame constraint that is defined by other confidence scores of other object detection decisions from the video data that are acquired by the same camera at different times; a cross-view constraint defined by other confidence scores of other object detections in the video data from another camera with an overlapping field-of-view; and a cross-object constraint defined by a sequential context of a linear array of the objects, spatial attributes of the objects and the determined speed of the movement of the objects relative to the cameras.
摘要翻译：视频图像数据从同步摄像机获取，该相机具有通过线性阵列中的场景图像以确定的速度移动通过相机的对象的重叠视图。处理单元产生与相机视频流数据的帧内的置信度分数相关联的一个或多个对象检测。可信度分数被修改为约束上下文的函数，包括由不同时间由同一相机获取的视频数据的其他对象检测决定的其他置信度分数定义的跨帧约束; 由具有重叠视场的另一相机的视频数据中的其他对象检测的其他置信度得分定义的横视约束; 以及由对象的线性阵列，对象的空间属性和所确定的对象相对于照相机的移动速度的顺序上下文定义的跨对象约束。

25. 发明申请

US20130329049A1 MULTISENSOR EVIDENCE INTEGRATION AND OPTIMIZATION IN OBJECT INSPECTION 有权
标题翻译：多媒体证据集成和对象检查优化
公开(公告)号：US20130329049A1
公开(公告)日：2013-12-12
申请号：US13489489
申请日：2012-06-06
申请人： Norman Haas , Ying Li , Charles A. Otto , Sharathchandra U. Pankanti , Hoang Trinh
发明人： Norman Haas , Ying Li , Charles A. Otto , Sharathchandra U. Pankanti , Hoang Trinh
IPC分类号： H04N7/18
CPC分类号： B61L23/042
摘要： Video image data is acquired from synchronized cameras having overlapping views of objects moving past the cameras through a scene image in a linear array and with a determined speed. Processing units generate one or more object detections associated with confidence scores within frames of the camera video stream data. The confidence scores are modified as a function of constraint contexts including a cross-frame constraint that is defined by other confidence scores of other object detection decisions from the video data that are acquired by the same camera at different times; a cross-view constraint defined by other confidence scores of other object detections in the video data from another camera with an overlapping field-of-view; and a cross-object constraint defined by a sequential context of a linear array of the objects, spatial attributes of the objects and the determined speed of the movement of the objects relative to the cameras.
摘要翻译：视频图像数据从同步摄像机获取，该相机具有通过线性阵列中的场景图像以确定的速度移动通过相机的对象的重叠视图。处理单元产生与相机视频流数据的帧内的置信度分数相关联的一个或多个对象检测。可信度分数被修改为约束上下文的函数，包括由不同时间由同一相机获取的视频数据的其他对象检测决定的其他置信度分数定义的跨帧约束; 由具有重叠视场的另一相机的视频数据中的其他对象检测的其他置信度得分定义的横视约束; 以及由对象的线性阵列，对象的空间属性和所确定的对象相对于照相机的移动速度的顺序上下文定义的跨对象约束。

26. 发明授权

US09158976B2 Efficient retrieval of anomalous events with priority learning 有权
标题翻译：优先学习有效地检索异常事件
公开(公告)号：US09158976B2
公开(公告)日：2015-10-13
申请号：US13110331
申请日：2011-05-18
申请人： Ankur Datta , Balamanohar Paluri , Sharathchandra U. Pankanti , Yun Zhai
发明人： Ankur Datta , Balamanohar Paluri , Sharathchandra U. Pankanti , Yun Zhai
IPC分类号： G08B13/196 , G06K9/00 , G06F19/24 , G06K9/62
CPC分类号： G06K9/00771 , G06F19/24 , G06K9/6284 , G06T7/251 , G08B13/19606
摘要： Local models learned from anomaly detection are used to rank detected anomalies. The local models include image feature values extracted from an image field of video image data with respect to different predefined spatial and temporal local units, wherein anomaly results are determined by failures to fit to applied anomaly detection module local models. Image features values extracted from the image field local units associated with anomaly results are normalized, and image feature values extracted from the image field local units are clustered. Weights for anomaly results are learned as a function of the relations of the normalized extracted image feature values to the clustered image feature values. The normalized values are multiplied by the learned weights to generate ranking values to rank the anomalies.
摘要翻译：从异常检测中获取的局部模型用于对检测到的异常进行排序。本地模型包括从视频图像数据的图像字段提取的关于不同的预定空间和时间局部单位的图像特征值，其中异常结果由适合于应用的异常检测模块本地模型的失败确定。从与异常结果相关联的图像场本地单元提取的图像特征值被归一化，并且从图像场本地单元提取的图像特征值被聚类。根据归一化提取的图像特征值与聚类图像特征值的关系来学习异常结果的权重。归一化值乘以学习权重以产生排序值以排除异常。

27. 发明授权

US09050984B2 Anomalous railway component detection 有权
标题翻译：异常铁路部件检测
公开(公告)号：US09050984B2
公开(公告)日：2015-06-09
申请号：US13451610
申请日：2012-04-20
申请人： Ying Li , Sharathchandra U. Pankanti
发明人： Ying Li , Sharathchandra U. Pankanti
IPC分类号： G06K9/00 , B61L23/04 , G06T7/00
CPC分类号： B61L23/042 , G06T7/0004 , G06T7/194 , G06T2207/30136 , G06T2207/30164 , G06T2207/30242 , G06T2207/30252
摘要： A method and system for inspecting railway components. The method includes receiving a stream of images containing railway components, detecting a railway component in each image, generating a plurality of feature vectors for each railway component image, measuring the dissimilarity between the railway component and a set of railway components detected in preceding images, in a sliding window, based on the feature vectors.
摘要翻译：检查铁路部件的方法和系统。该方法包括接收包含铁路部件的图像流，检测每个图像中的铁路部件，为每个铁路部件图像生成多个特征向量，测量铁路部件与在先前图像中检测到的一组铁路部件之间的不相似性，在滑动窗口中，基于特征向量。

28. 发明授权

US08751414B2 Identifying abnormalities in resource usage 失效
标题翻译：识别资源使用异常
公开(公告)号：US08751414B2
公开(公告)日：2014-06-10
申请号：US13100868
申请日：2011-05-04
申请人： Ankur Datta , Charles A. Otto , Sharathchandra U. Pankanti
发明人： Ankur Datta , Charles A. Otto , Sharathchandra U. Pankanti
IPC分类号： G06N5/04 , G06F11/07 , G06F11/30
CPC分类号： G06F11/0751 , G06F11/3058 , G06F11/3082
摘要： A method, data processing system, and computer program product for identifying abnormalities in data. A model representing a plurality of modes for an activity generated from training data is retrieved. The training data includes a first plurality of measurements of a first performance of the activity over a period of time. Each of the plurality of modes is identified as one of normal and abnormal. Activity data including a second plurality of measurements of a second performance of the activity is received. A portion of the activity data is compared with the plurality of modes in the model. A notification of an abnormality in the second performance of the activity is generated in response to an identification that the portion of the activity data matches a mode in the plurality of modes identified as abnormal. Confirmation of the abnormality is requested via a user interface.
摘要翻译：一种用于识别数据异常的方法，数据处理系统和计算机程序产品。检索表示从训练数据生成的活动的多个模式的模型。训练数据包括在一段时间内第一次执行活动的测量。多个模式中的每一个被标识为正常和异常之一。接收包括活动的第二次执行的第二多个测量的活动数据。将活动数据的一部分与模型中的多个模式进行比较。响应于识别出活动数据的一部分与被识别为异常的多个模式中的模式相匹配的标识来生成第二次活动的异常的通知。通过用户界面要求确认异常。

29. 发明授权

US08061603B2 Controlling shopper checkout throughput 失效
标题翻译：控制购物者结帐吞吐量
公开(公告)号：US08061603B2
公开(公告)日：2011-11-22
申请号：US12052051
申请日：2008-03-20
申请人： Jonathan H. Connell, II , Myron D. Flickner , Norman Haas , Arun Hampapur , Charles A. Otto , Sharathchandra U. Pankanti
发明人： Jonathan H. Connell, II , Myron D. Flickner , Norman Haas , Arun Hampapur , Charles A. Otto , Sharathchandra U. Pankanti
IPC分类号： G06K15/00
CPC分类号： G07G1/0054
摘要： The present invention provides a solution for controlling checkout throughput. Specifically, under the present invention a set of attributes of an item will be obtained. An analysis of the set of attributes will then be performed to determine whether an inconsistency exists based on a tolerance threshold. Such analysis can include, for example, comparing the weight of the item to its expected weight. This can help determine if a mistake has been made or fraud has been committed. Whether an inconsistency is observed is based on a tolerance threshold. For example, a certain deviation (i.e., the tolerance threshold) from an expected value could be allowed to could be allowed to reduce errors during the checkout process and thereby increase checkout throughput. As such, the present invention allows the tolerance threshold to be tuned based on a desired checkout throughput.
摘要翻译：本发明提供了一种用于控制检验吞吐量的解决方案。具体地说，在本发明中，将获得一组物品的属性。然后将执行对该组属性的分析，以基于公差阈值确定是否存在不一致。这种分析可以包括例如将物品的重量与其预期重量进行比较。这可以帮助确定是否发生了错误或已经犯了欺诈。观察到不一致是基于公差阈值。例如，可以允许从期望值的一定偏差（即，容限阈值）被允许在结算过程期间减少错误，从而增加结帐吞吐量。因此，本发明允许基于期望的结算吞吐量来调整公差阈值。

30. 发明申请

US20100157051A1 SYSTEM AND METHOD FOR DETECTING AND DETERRING RFID TAG RELATED FRAUD 审中-公开
标题翻译：用于检测和确定RFID标签相关法规的系统和方法
公开(公告)号：US20100157051A1
公开(公告)日：2010-06-24
申请号：US12342482
申请日：2008-12-23
申请人： Sharathchandra U. Pankanti
发明人： Sharathchandra U. Pankanti
IPC分类号： H04N9/47
CPC分类号： G06Q10/00
摘要： An approach that allows for detecting and deterring RFID tag related fraud is provided. In one embodiment, there is a generating tool configured to generate a set of tag-item models based on results of a cumulative training process; detecting tool configured to detect discrepancy between an expected appearance of the item as determined from analyzing corresponding tag-item model and an actual appearance of the item as captured by color camera, and an acknowledging tool configured to acknowledge said detected discrepancy.
摘要翻译：提供了一种允许检测和阻止RFID标签相关欺诈的方法。在一个实施例中，存在一种生成工具，其被配置为基于累积训练过程的结果生成一组标签项模型; 检测工具，被配置为检测从分析对应的标签项目模型确定的项目的预期外观与由彩色照相机捕获的项目的实际外观之间的差异;以及确认工具，其被配置为确认所检测到的差异。

你已经成功收藏专利！

检索式保存成功!

IPRDB

热门服务

关于我们

友情链接

联系方式