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1. 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。

2. 发明申请

US20120242832A1 VEHICLE HEADLIGHT MANAGEMENT 失效
标题翻译：车灯管理
公开(公告)号：US20120242832A1
公开(公告)日：2012-09-27
申请号：US13071768
申请日：2011-03-25
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： H04N7/18 , G06K9/00 , B60Q1/04
CPC分类号： B60Q1/143 , B60Q2300/312 , B60Q2300/3321 , B60Q2300/41 , B60Q2300/42 , G06K9/00825
摘要： A method, data processing system, and computer program product for managing a headlight on a vehicle are presented. An image of an area in front of the vehicle is received. A first set of features is identified in the received image. The first set of features in the received image is compared with a number of sets of features from a plurality of previous images. Each image in the plurality of previous images is associated with a headlight setting. A second set of features from a previous image in the plurality of previous images matching the first set of features in the received image is identified. A determination is made whether to change a setting for the headlight on the vehicle based on the headlight setting associated with the previous image.
摘要翻译：提出了一种用于管理车辆前灯的方法，数据处理系统和计算机程序产品。接收车辆前方的区域的图像。在接收到的图像中识别出第一组特征。将接收到的图像中的第一组特征与来自多个先前图像的多个特征集进行比较。多个先前图像中的每个图像与头灯设置相关联。识别与接收到的图像中的第一组特征匹配的多个先前图像中的先前图像的第二组特征。根据与先前图像相关联的头灯设置来确定是否改变车辆前灯的设置。

3. 发明授权

US08605154B2 Vehicle headlight management 失效
标题翻译：车头灯管理
公开(公告)号：US08605154B2
公开(公告)日：2013-12-10
申请号：US13071768
申请日：2011-03-25
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： H04N7/18 , H04N5/33 , F21V23/04
CPC分类号： B60Q1/143 , B60Q2300/312 , B60Q2300/3321 , B60Q2300/41 , B60Q2300/42 , G06K9/00825
摘要： A method, data processing system, and computer program product for managing a headlight on a vehicle are presented. An image of an area in front of the vehicle is received. A first set of features is identified in the received image. The first set of features in the received image is compared with a number of sets of features from a plurality of previous images. Each image in the plurality of previous images is associated with a headlight setting. A second set of features from a previous image in the plurality of previous images matching the first set of features in the received image is identified. A determination is made whether to change a setting for the headlight on the vehicle based on the headlight setting associated with the previous image.
摘要翻译：提出了一种用于管理车辆前灯的方法，数据处理系统和计算机程序产品。接收车辆前方的区域的图像。在接收到的图像中识别出第一组特征。将接收到的图像中的第一组特征与来自多个先前图像的多个特征集进行比较。多个先前图像中的每个图像与头灯设置相关联。识别与接收到的图像中的第一组特征匹配的多个先前图像中的先前图像的第二组特征。根据与先前图像相关联的头灯设置来确定是否改变车辆前灯的设置。

4. 发明申请

US20120284211A1 IDENTIFYING ABNORMALITIES IN RESOURCE USAGE 失效
标题翻译：识别资源使用异常
公开(公告)号：US20120284211A1
公开(公告)日：2012-11-08
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： G06F15/18 , G06F17/00 , G06N5/04
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。

5. 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。

6. 发明授权

US09002060B2 Object retrieval in video data using complementary detectors 有权
标题翻译：使用互补检测器对视频数据进行对象检索
公开(公告)号：US09002060B2
公开(公告)日：2015-04-07
申请号：US13535409
申请日：2012-06-28
申请人： Ankur Datta , Rogerio S. Feris , Sharathchandra U. Pankanti , Yun Zhai
发明人： Ankur Datta , Rogerio S. Feris , Sharathchandra U. Pankanti , Yun Zhai
IPC分类号： G06K9/00 , 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。

7. 发明申请

US20140003708A1 OBJECT RETRIEVAL IN VIDEO DATA USING COMPLEMENTARY DETECTORS 有权
标题翻译：使用完全检测器的视频数据中的对象检索
公开(公告)号：US20140003708A1
公开(公告)日：2014-01-02
申请号：US13535409
申请日：2012-06-28
申请人： Ankur Datta , Rogerio S. Feris , Sharathchandra U. Pankanti , Yun Zhai
发明人： Ankur Datta , Rogerio S. Feris , Sharathchandra U. Pankanti , Yun Zhai
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。

8. 发明授权

US08380711B2 Hierarchical ranking of facial attributes 失效
标题翻译：面部属性的分级排名
公开(公告)号：US08380711B2
公开(公告)日：2013-02-19
申请号：US13045092
申请日：2011-03-10
申请人： Ankur Datta , Rogerio S. Feris , Sharathchandra U. Pankanti , Daniel A. Vaquero
发明人： Ankur Datta , Rogerio S. Feris , Sharathchandra U. Pankanti , Daniel A. Vaquero
IPC分类号： G06F7/00 , G06F17/30
CPC分类号： G06F17/30277 , G06F17/16 , G06F17/30256 , G06F17/30265 , G06F17/30271 , G06F17/30528 , G06F17/3053 , G06F17/3069 , G06K9/00268 , G06K9/6228 , Y10S707/915
摘要： In response to a query of discernible facial attributes, the locations of distinct and different facial regions are estimated from face image data, each relevant to different attributes. Different features are extracted from the estimated facial regions from database facial images, which are ranked in base layer rankings by matching feature vectors in a bipartite graph to a base layer ranking sequence as a function of edge weights parameterized by an associated base layer parameter vector. Second-layer rankings define second-layer attribute vectors as bilinear combinations of the base-layer feature vectors and associated base layer parameter vectors for common attributes, which are matched in a bipartite graph to a second-layer ranking sequence as a function of edge weights parameterized by associated second-layer parameter vectors. The images are thus ranked for relevance to the query as a function of fusing the second-layer rankings.
摘要翻译：响应于可辨别的面部属性的查询，根据与不同属性相关的面部图像数据来估计不同和不同面部区域的位置。从数据库面部图像从估计的面部区域提取不同的特征，其通过将二分图中的特征向量与基本层排序序列相匹配来排列在基本层排名中，作为由相关联的基本层参数向量参数化的边缘权重的函数。第二层次排列将第二层属性向量定义为用于共同属性的基层特征向量和相关联的基层参数向量的双线性组合，其在二分图中与作为边权的函数的第二层排序序列匹配由相关的第二层参数向量参数化。因此，这些图像作为将第二层排名融合的功能被排列为与查询相关。

9. 发明申请

US20120281873A1 INCORPORATING VIDEO META-DATA IN 3D MODELS 有权
标题翻译：在3D模型中加入视频元数据
公开(公告)号：US20120281873A1
公开(公告)日：2012-11-08
申请号：US13101401
申请日：2011-05-05
申请人： Lisa M. Brown , Ankur Datta , Rogerio S. Feris , Sharathchandra U. Pankanti
发明人： Lisa M. Brown , Ankur Datta , Rogerio S. Feris , Sharathchandra U. Pankanti
IPC分类号： G06K9/00
CPC分类号： G06T7/20 , G06K9/00208 , G06T7/251 , G06T13/20 , G06T17/00 , G06T19/006
摘要： A moving object detected and tracked within a field of view environment of a 2D data feed of a calibrated video camera is represented by a 3D model through localizing a centroid of the object and determining an intersection with a ground-plane within the field of view environment. An appropriate 3D mesh-based volumetric model for the object is initialized by using a back-projection of a corresponding 2D image as a function of the centroid and the determined ground-plane intersection. Nonlinear dynamics of a tracked motion path of the object are represented as a collection of different local linear models. A texture of the object is projected onto the 3D model, and 2D tracks of the object are upgraded to 3D motion to drive the 3D model by learning a weighted combination of the different local linear models that minimizes an image re-projection error of model movement.
摘要翻译：在校准摄像机的2D数据馈送的视野环境内检测和跟踪的移动物体由3D模型表示，其通过定位对象的质心并确定视场环境内的接地平面的交点。通过使用对应的2D图像的反投影作为质心和确定的地面交点的函数来初始化用于对象的适当的基于3D网格的体积模型。对象的跟踪运动路径的非线性动力学被表示为不同局部线性模型的集合。将对象的纹理投影到3D模型上，并且将对象的2D轨迹升级到3D运动，以通过学习不同局部线性模型的加权组合来驱动3D模型，从而最小化模型运动的图像重新投影误差。

10. 发明申请

US20120237081A1 ANOMALOUS PATTERN DISCOVERY 失效
标题翻译：异常图案发现
公开(公告)号：US20120237081A1
公开(公告)日：2012-09-20
申请号：US13049032
申请日：2011-03-16
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： G06K9/00
CPC分类号： G06K9/00778 , G06K9/00785 , G06K9/6284
摘要： A trajectory of movement of an object is tracked in a video data image field that is partitioned into a plurality of different grids. Global image features from video data relative to the trajectory are extracted and compared to a learned trajectory model to generate a global anomaly detection confidence decision value as a function of fitting to the learned trajectory model. Local image features are also extracted for each of the image field grids that include object trajectory, which are compared to learned feature models for the grids to generate local anomaly detection confidence decisions for each grid as a function of fitting to the learned feature models for the grids. The global anomaly detection confidence decision value and the local anomaly detection confidence decision values for the grids are into a fused anomaly decision with respect to the tracked object.
摘要翻译：在被划分成多个不同网格的视频数据图像字段中跟踪对象的移动轨迹。提取视频数据相对于轨迹的全局图像特征，并将其与已学习的轨迹模型进行比较，以生成与学习轨迹模型拟合的函数的全局异常检测置信判定值。对于包括对象轨迹的每个图像场网格也提取局部图像特征，其与用于网格的学习特征模型进行比较，以针对每个网格生成局部异常检测置信决定，作为对于学习特征模型的拟合的函数，网格全局异常检测置信度判定值和网格的局部异常检测置信判定值相对于跟踪对象进入融合异常判定。

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