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公开(公告)号：US20120294511A1
公开(公告)日：2012-11-22
申请号：US13110331
申请日：2011-05-18
IPC分类号： 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。
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公开(公告)号：US20120274805A1
公开(公告)日：2012-11-01
申请号：US13097435
申请日：2011-04-29
IPC分类号： H04N9/73
摘要： Methods and apparatus are provided for color correction of images. One or more colors in an image obtained from a static video camera are corrected by obtaining one or more historical background models from one or more prior images obtained from the static video camera; obtaining a live background model and a live foreground model from one or more current images obtained from the static video camera; generating a reference image from the one or more historical background models; and processing the reference image, the live background model, and the live foreground model to generate a set of color corrected foreground objects in the image. The set of color corrected foreground objects is optionally processed to classify a color of at least one of the foreground objects.
摘要翻译： 为图像的颜色校正提供了方法和装置。 通过从从静态摄像机获得的一个或多个先前图像获得一个或多个历史背景模型来校正从静态摄像机获得的图像中的一种或多种颜色; 从静态摄像机获得的一个或多个当前图像获得实况背景模型和实况前景模型; 从所述一个或多个历史背景模型生成参考图像; 以及处理参考图像，实况背景模型和实况前景模型以生成图像中的一组颜色校正的前景对象。 可选地处理该组色彩校正的前景对象以对至少一个前景对象的颜色进行分类。
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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：US09260122B2
公开(公告)日：2016-02-16
申请号：US13489489
申请日：2012-06-06
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。
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公开(公告)号：US09002060B2
公开(公告)日：2015-04-07
申请号：US13535409
申请日：2012-06-28
CPC分类号： G06K9/00771 , G06K9/00718 , G06K9/00758 , G06K9/6215 , G06K9/6218 , G06K9/6256 , G06K9/6262 , G06K2009/00738
摘要： Automatic object retrieval from input video is based on learned, complementary detectors created for each of a plurality of different motionlet clusters. The motionlet clusters are partitioned from a dataset of training vehicle images as a function of determining that vehicles within each of the scenes of the images in each cluster share similar two-dimensional motion direction attributes within their scenes. To train the complementary detectors, a first detector is trained on motion blobs of vehicle objects detected and collected within each of the training dataset vehicle images within the motionlet cluster via a background modeling process; a second detector is trained on each of the training dataset vehicle images within the motionlet cluster that have motion blobs of the vehicle objects but are misclassified by the first detector; and the training repeats until all of the training dataset vehicle images have been eliminated as false positives or correctly classified.
摘要翻译： 从输入视频自动对象检索是基于为多个不同的运动集群中的每一个创建的学习的互补检测器。 作为确定每个群集中的图像的每个场景内的车辆在其场景内共享类似的二维运动方向属性的函数的函数，将运动群集从训练车辆图像的数据集分割。 训练互补检测器，对第一检测器进行训练，以通过背景建模过程在运动组内的每个训练数据集车辆图像内检测和收集的车辆物体的运动斑点进行训练; 对具有车辆对象的运动斑点但由第一检测器错误分类的运动集群内的训练数据集车辆图像上的每一个训练第二检测器; 并且训练重复，直到所有训练数据集车辆图像已被消除为假阳性或正确分类为止。
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公开(公告)号：US08824791B2
公开(公告)日：2014-09-02
申请号：US13097435
申请日：2011-04-29
摘要： Methods and apparatus are provided for color correction of images. One or more colors in an image obtained from a static video camera are corrected by obtaining one or more historical background models from one or more prior images obtained from the static video camera; obtaining a live background model and a live foreground model from one or more current images obtained from the static video camera; generating a reference image from the one or more historical background models; and processing the reference image, the live background model, and the live foreground model to generate a set of color corrected foreground objects in the image. The set of color corrected foreground objects is optionally processed to classify a color of at least one of the foreground objects.
摘要翻译： 为图像的颜色校正提供了方法和装置。 通过从从静态摄像机获得的一个或多个先前图像获得一个或多个历史背景模型来校正从静态摄像机获得的图像中的一种或多种颜色; 从静态摄像机获得的一个或多个当前图像获得实况背景模型和实况前景模型; 从所述一个或多个历史背景模型生成参考图像; 以及处理参考图像，实况背景模型和实况前景模型以生成图像中的一组颜色校正的前景对象。 可选地处理该组色彩校正的前景对象以对至少一个前景对象的颜色进行分类。
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公开(公告)号：US08811663B2
公开(公告)日：2014-08-19
申请号：US13150952
申请日：2011-06-01
申请人： Lisa M. Brown , Rogerio S. Feris , Sharathchandra U. Pankanti , James Petterson , Behjat Siddiquie
发明人： Lisa M. Brown , Rogerio S. Feris , Sharathchandra U. Pankanti , James Petterson , Behjat Siddiquie
CPC分类号： G06K9/6256 , G06K9/00778 , G06K9/00785 , G06K9/6264
摘要： Methods and systems are provided for object detection. A method includes automatically collecting a set of training data images from a plurality of images. The method further includes generating occluded images. The method also includes storing in a memory the generated occluded images as part of the set of training data images, and training an object detector using the set of training data images stored in the memory. The method additionally includes detecting an object using the object detector, the object detector detecting the object based on the set of training data images stored in the memory.
摘要翻译： 为对象检测提供了方法和系统。 一种方法包括从多个图像自动收集一组训练数据图像。 该方法还包括产生遮挡图像。 该方法还包括将生成的遮挡图像作为训练数据图像集合的一部分存储在存储器中，并且使用存储在存储器中的训练数据图像集训练对象检测器。 该方法还包括使用对象检测器检测对象，对象检测器基于存储在存储器中的训练数据图像集合来检测对象。
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78.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
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。
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公开(公告)号：US20140003708A1
公开(公告)日：2014-01-02
申请号：US13535409
申请日：2012-06-28
IPC分类号： G06K9/62
CPC分类号： G06K9/00771 , G06K9/00718 , G06K9/00758 , G06K9/6215 , G06K9/6218 , G06K9/6256 , G06K9/6262 , G06K2009/00738
摘要： Automatic object retrieval from input video is based on learned, complementary detectors created for each of a plurality of different motionlet clusters. The motionlet clusters are partitioned from a dataset of training vehicle images as a function of determining that vehicles within each of the scenes of the images in each cluster share similar two-dimensional motion direction attributes within their scenes. To train the complementary detectors, a first detector is trained on motion blobs of vehicle objects detected and collected within each of the training dataset vehicle images within the motionlet cluster via a background modeling process; a second detector is trained on each of the training dataset vehicle images within the motionlet cluster that have motion blobs of the vehicle objects but are misclassified by the first detector; and the training repeats until all of the training dataset vehicle images have been eliminated as false positives or correctly classified.
摘要翻译： 从输入视频自动对象检索是基于为多个不同的运动集群中的每一个创建的学习的互补检测器。 作为确定每个群集中的图像的每个场景内的车辆在其场景内共享类似的二维运动方向属性的函数的函数，将运动群集从训练车辆图像的数据集分割。 训练互补检测器，对第一检测器进行训练，以通过背景建模过程在运动组内的每个训练数据集车辆图像内检测和收集的车辆物体的运动斑点进行训练; 对具有车辆对象的运动斑点但由第一检测器错误分类的运动集群内的训练数据集车辆图像上的每一个训练第二检测器; 并且训练重复，直到所有训练数据集车辆图像已被消除为假阳性或正确分类为止。
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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
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。
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