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1. 发明申请

US20120093398A1 SYSTEM AND METHOD FOR MULTI-AGENT EVENT DETECTION AND RECOGNITION 审中-公开
标题翻译：用于多事件事件检测和识别的系统和方法
公开(公告)号：US20120093398A1
公开(公告)日：2012-04-19
申请号：US13336815
申请日：2011-12-23
申请人： Hui Cheng , Changjiang Yang , Harpreet Singh Sawhney , Feng Han
发明人： Hui Cheng , Changjiang Yang , Harpreet Singh Sawhney , Feng Han
IPC分类号： G06K9/62 , G06K9/00
CPC分类号： G06F16/70 , G06K9/00771 , G06K9/4642 , G06K9/6292
摘要： A method and system for creating a histogram of oriented occurrences (HO2) is disclosed. A plurality of entities in at least one image are detected and tracked. One of the plurality of entities is designated as a reference entity. A local 2-dimensional ground plane coordinate system centered on and oriented with respect to the reference entity is defined. The 2-dimensional ground plane is partitioned into a plurality of non-overlapping bins, the bins forming a histogram, a bin tracking a number of occurrences of an entity class. An occurrence of at least one other entity of the plurality of entities located in the at least one image may be associated with one of the plurality of non-overlapping bins. A number of occurrences of entities of at least one entity class in at least one bin may be into a vector to define an HO2 feature.
摘要翻译：公开了一种用于创建定向事件直方图（HO2）的方法和系统。检测并跟踪至少一个图像中的多个实体。多个实体之一被指定为参照实体。定义以参考实体为中心并定向的局部二维地面坐标系。二维接地平面被划分成多个不重叠的箱体，该箱体形成一个直方图，一个箱子跟踪一个实体类的出现次数。位于所述至少一个图像中的所述多个实体中的至少一个其他实体的出现可以与所述多个非重叠区域中的一个相关联。在至少一个箱中的至少一个实体类的实体的多个出现可以被转换为向量以定义HO2特征。

2. 发明授权

US09569531B2 System and method for multi-agent event detection and recognition 有权
标题翻译：多代理事件检测和识别的系统和方法
公开(公告)号：US09569531B2
公开(公告)日：2017-02-14
申请号：US12489667
申请日：2009-06-23
申请人： Hui Cheng , Changjiang Yan , Harpreet Singh Sawhney , Feng Han
发明人： Hui Cheng , Changjiang Yan , Harpreet Singh Sawhney , Feng Han
IPC分类号： G06F17/30 , G06K9/00 , G06K9/46 , G06K9/62
CPC分类号： G06F17/30781 , G06K9/00771 , G06K9/4642 , G06K9/6292
摘要： A method and system for creating a histogram of oriented occurrences (HO2) is disclosed. A plurality of entities in at least one image are detected and tracked. One of the plurality of entities is designated as a reference entity. A local 2-dimensional ground plane coordinate system centered on and oriented with respect to the reference entity is defined. The 2-dimensional ground plane is partitioned into a plurality of non-overlapping bins, the bins forming a histogram, a bin tracking a number of occurrences of an entity class. An occurrence of at least one other entity of the plurality of entities located in the at least one image may be associated with one of the plurality of non-overlapping bins. A number of occurrences of entities of at least one entity class in at least one bin may be into a vector to define an HO2 feature.
摘要翻译：公开了一种用于创建定向事件直方图（HO2）的方法和系统。检测并跟踪至少一个图像中的多个实体。多个实体之一被指定为参照实体。定义以参考实体为中心并定向的局部二维地面坐标系。二维接地平面被划分成多个不重叠的箱体，该箱体形成一个直方图，一个箱子跟踪一个实体类的出现次数。位于所述至少一个图像中的所述多个实体中的至少一个其他实体的出现可以与所述多个非重叠区域中的一个相关联。在至少一个箱中的至少一个实体类的实体的多个出现可以被转换为向量以定义HO2特征。

3. 发明授权

US08233704B2 Exemplar-based heterogeneous compositional method for object classification 有权
标题翻译：用于对象分类的基于示例的异构组合方法
公开(公告)号：US08233704B2
公开(公告)日：2012-07-31
申请号：US12136138
申请日：2008-06-10
申请人： Feng Han , Hui Cheng , Jiangjian Xiao , Harpreet Singh Sawhney
发明人： Feng Han , Hui Cheng , Jiangjian Xiao , Harpreet Singh Sawhney
IPC分类号： G06K9/62 , G06E1/00 , G06E3/00 , G06F15/18 , G06G7/00
CPC分类号： G06K9/3241 , G06K9/6256 , G06K9/6292
摘要： A method for automatically generating a strong classifier for determining whether at least one object is detected in at least one image is disclosed, comprising the steps of: (a) receiving a data set of training images having positive images; (b) randomly selecting a subset of positive images from the training images to create a set of candidate exemplars, wherein said positive images include at least one object of the same type as the object to be detected; (c) training a weak classifier based on at least one of the candidate exemplars, said training being based on at least one comparison of a plurality of heterogeneous compositional features located in the at least one image and corresponding heterogeneous compositional features in the one of set of candidate exemplars; (d) repeating steps (c) for each of the remaining candidate exemplars; and (e) combining the individual classifiers into a strong classifier, wherein the strong classifier is configured to determine the presence or absence in an image of the object to be detected.
摘要翻译：公开了一种用于自动生成强分类器以确定在至少一个图像中是否检测到至少一个对象的方法，包括以下步骤：（a）接收具有正图像的训练图像的数据集; （b）从所述训练图像中随机选择正图像的子集以创建一组候选样本，其中所述正图像包括与所述待检测对象相同类型的至少一个对象; （c）基于所述候选样本中的至少一个来训练弱分类器，所述训练基于位于所述至少一个图像中的多个异质成分特征和所述一个图像中的一个中的对应的异质组成特征的至少一个比较候选人样本（d）为每个其余的候选样本重复步骤（c）; 以及（e）将各个分类器组合成强分类器，其中强分类器被配置为确定待检测对象的图像中的存在或不存在。

4. 发明申请

US20080310737A1 EXEMPLAR-BASED HETEROGENEOUS COMPOSITIONAL METHOD FOR OBJECT CLASSIFICATION 有权
标题翻译：用于对象分类的基于EXEMPLAR的异构组合方法
公开(公告)号：US20080310737A1
公开(公告)日：2008-12-18
申请号：US12136138
申请日：2008-06-10
申请人： Feng Han , Hui Cheng , Jiangjian Xiao , Harpreet Singh Sawhney
发明人： Feng Han , Hui Cheng , Jiangjian Xiao , Harpreet Singh Sawhney
IPC分类号： G06K9/62
CPC分类号： G06K9/3241 , G06K9/6256 , G06K9/6292
摘要： A method for automatically generating a strong classifier for determining whether at least one object is detected in at least one image is disclosed, comprising the steps of: (a) receiving a data set of training images having positive images; (b) randomly selecting a subset of positive images from the training images to create a set of candidate exemplars, wherein said positive images include at least one object of the same type as the object to be detected; (c) training a weak classifier based on at least one of the candidate exemplars, said training being based on at least one comparison of a plurality of heterogeneous compositional features located in the at least one image and corresponding heterogeneous compositional features in the one of set of candidate exemplars; (d) repeating steps (c) for each of the remaining candidate exemplars; and (e) combining the individual classifiers into a strong classifier, wherein the strong classifier is configured to determine the presence or absence in an image of the object to be detected.
摘要翻译：公开了一种用于自动生成强分类器以确定在至少一个图像中是否检测到至少一个对象的方法，包括以下步骤：（a）接收具有正图像的训练图像的数据集; （b）从所述训练图像中随机选择正图像的子集以创建一组候选样本，其中所述正图像包括与所述待检测对象相同类型的至少一个对象; （c）基于所述候选样本中的至少一个训练弱分类器，所述训练基于位于所述至少一个图像中的多个异质成分特征和所述一个图像中的一个中的对应的异质组成特征的至少一个比较候选人样本（d）为每个其余的候选样本重复步骤（c）; 以及（e）将各个分类器组合成强分类器，其中强分类器被配置为确定待检测对象的图像中的存在或不存在。

5. 发明申请

US20090319560A1 SYSTEM AND METHOD FOR MULTI-AGENT EVENT DETECTION AND RECOGNITION 有权
标题翻译：用于多事件事件检测和识别的系统和方法
公开(公告)号：US20090319560A1
公开(公告)日：2009-12-24
申请号：US12489667
申请日：2009-06-23
申请人： Hui Cheng , Changjiang Yan , Harpreet Singh Sawhney , Feng Han
发明人： Hui Cheng , Changjiang Yan , Harpreet Singh Sawhney , Feng Han
IPC分类号： G06F17/30
CPC分类号： G06F17/30781 , G06K9/00771 , G06K9/4642 , G06K9/6292
摘要： A method and system for creating a histogram of oriented occurrences (HO2) is disclosed. A plurality of entities in at least one image are detected and tracked. One of the plurality of entities is designated as a reference entity. A local 2-dimensional ground plane coordinate system centered on and oriented with respect to the reference entity is defined. The 2-dimensional ground plane is partitioned into a plurality of non-overlapping bins, the bins forming a histogram, a bin tracking a number of occurrences of an entity class. An occurrence of at least one other entity of the plurality of entities located in the at least one image may be associated with one of the plurality of non-overlapping bins. A number of occurrences of entities of at least one entity class in at least one bin may be into a vector to define an HO2 feature.
摘要翻译：公开了一种用于创建定向事件直方图（HO2）的方法和系统。检测并跟踪至少一个图像中的多个实体。多个实体之一被指定为参照实体。定义以参考实体为中心并定向的局部二维地面坐标系。二维接地平面被划分成多个不重叠的箱体，该箱体形成一个直方图，一个箱子跟踪一个实体类的出现次数。位于所述至少一个图像中的所述多个实体中的至少一个其他实体的出现可以与所述多个非重叠区域中的一个相关联。在至少一个箱中的至少一个实体类的实体的多个出现可以被转换为向量以定义HO2特征。

6. 发明授权

US08634638B2 Real-time action detection and classification 有权
标题翻译：实时动作检测与分类
公开(公告)号：US08634638B2
公开(公告)日：2014-01-21
申请号：US12488911
申请日：2009-06-22
申请人： Feng Han , Hui Cheng , Jiangjian Xiao , Harpreet Singh Sawhney , Sang-Hack Jung , Rakesh Kumar , Yanlin Guo
发明人： Feng Han , Hui Cheng , Jiangjian Xiao , Harpreet Singh Sawhney , Sang-Hack Jung , Rakesh Kumar , Yanlin Guo
IPC分类号： G06K9/62
CPC分类号： G06K9/00342
摘要： The present invention relates to a method and system for creating a strong classifier based on motion patterns wherein the strong classifier may be used to determine an action being performed by a body in motion. When creating the strong classifier, action classification is performed by measuring similarities between features within motion patterns. Embodiments of the present invention may utilize candidate part-based action sets and training samples to train one or more weak classifiers that are then used to create a strong classifier.
摘要翻译：本发明涉及一种用于基于运动模式创建强分类器的方法和系统，其中强分类器可用于确定由运动中的身体执行的动作。当创建强分类器时，通过测量运动模式中的特征之间的相似度来执行动作分类。本发明的实施例可以利用基于候选部分的动作集和训练样本来训练一个或多个弱分类器，然后将其用于创建强分类器。

7. 发明申请

US20090316983A1 Real-Time Action Detection and Classification 有权
标题翻译：实时行动检测与分类
公开(公告)号：US20090316983A1
公开(公告)日：2009-12-24
申请号：US12488911
申请日：2009-06-22
申请人： Feng Han , Hui Cheng , Jiangjian Xiao , Harpreet Singh Sawhney , Sang-Hack Jung , Rakesh Kumar , Yanlin Guo
发明人： Feng Han , Hui Cheng , Jiangjian Xiao , Harpreet Singh Sawhney , Sang-Hack Jung , Rakesh Kumar , Yanlin Guo
IPC分类号： G06K9/62
CPC分类号： G06K9/00342
摘要： The present invention relates to a method and system for creating a strong classifier based on motion patterns wherein the strong classifier may be used to determine an action being performed by a body in motion. When creating the strong classifier, action classification is performed by measuring similarities between features within motion patterns. Embodiments of the present invention may utilize candidate part-based action sets and training samples to train one or more weak classifiers that are then used to create a strong classifier.
摘要翻译：本发明涉及一种用于基于运动模式创建强分类器的方法和系统，其中强分类器可用于确定由运动中的身体执行的动作。当创建强分类器时，通过测量运动模式中的特征之间的相似度来执行动作分类。本发明的实施例可以利用基于候选部分的动作集和训练样本来训练一个或多个弱分类器，然后将其用于创建强分类器。

8. 发明申请

US20120106800A1 3-D MODEL BASED METHOD FOR DETECTING AND CLASSIFYING VEHICLES IN AERIAL IMAGERY 有权
标题翻译：用于在航空影像中检测和分类车辆的基于3-D模型的方法
公开(公告)号：US20120106800A1
公开(公告)日：2012-05-03
申请号：US12913861
申请日：2010-10-28
申请人： Saad Masood Khan , Hui Cheng , Dennis Lee Matthies , Harpreet Singh Sawhney , Sang-Hack Jung , Chris Broaddus , Bogdan Calin Mihai Matei , Ajay Divakaran
发明人： Saad Masood Khan , Hui Cheng , Dennis Lee Matthies , Harpreet Singh Sawhney , Sang-Hack Jung , Chris Broaddus , Bogdan Calin Mihai Matei , Ajay Divakaran
IPC分类号： G06K9/68
CPC分类号： G06K9/00785 , B64C39/024 , B64C2201/123 , B64D47/08 , G06K9/00651 , G06K9/4671 , G06K9/6269 , G06K9/6282 , G06T7/11 , G06T7/20 , G06T2207/10012
摘要： A computer implemented method for determining a vehicle type of a vehicle detected in an image is disclosed. An image having a detected vehicle is received. A number of vehicle models having salient feature points is projected on the detected vehicle. A first set of features derived from each of the salient feature locations of the vehicle models is compared to a second set of features derived from corresponding salient feature locations of the detected vehicle to form a set of positive match scores (p-scores) and a set of negative match scores (n-scores). The detected vehicle is classified as one of the vehicle models models based at least in part on the set of p-scores and the set of n-scores.
摘要翻译：公开了一种用于确定在图像中检测到的车辆的车辆类型的计算机实现的方法。接收具有检测到的车辆的图像。在检测到的车辆上投影出具有显着特征点的多个车辆模型。将来自车辆模型的每个突出特征位置的第一组特征与从所检测到的车辆的相应突出特征位置导出的第二组特征进行比较，以形成一组正的匹配得分（p分数）和一组负面比赛得分（n分）。检测到的车辆至少部分地基于p分数集合和n分数集合被分类为车辆模型模型之一。

9. 发明申请

US20120002869A1 System and method for detection of multi-view/multi-pose objects 有权
标题翻译：用于检测多视点/多姿态对象的系统和方法
公开(公告)号：US20120002869A1
公开(公告)日：2012-01-05
申请号：US13134885
申请日：2011-06-20
申请人： Feng Han , Ying Shan , Harpreet Singh Sawhney , Rakesh Kumar
发明人： Feng Han , Ying Shan , Harpreet Singh Sawhney , Rakesh Kumar
IPC分类号： G06K9/62
CPC分类号： G06K9/6256
摘要： The present invention provides a computer implemented process for detecting multi-view multi-pose objects. The process comprises training of a classifier for each intra-class exemplar, training of a strong classifier and combining the individual exemplar-based classifiers with a single objective function. This function is optimized using the two nested AdaBoost loops. The first loop is the outer loop that selects discriminative candidate exemplars. The second loop, the inner loop selects the discriminative candidate features on the selected exemplars to compute all weak classifiers for a specific position such as a view/pose. Then all the computed weak classifiers are automatically combined into a final classifier (strong classifier) which is the object to be detected.
摘要翻译：本发明提供了一种用于检测多视点多姿态对象的计算机实现过程。该过程包括针对每个类内样本的分类器的训练，强分类器的训练和将单个基于样本的分类器与单个目标函数组合。使用两个嵌套的AdaBoost循环来优化此功能。第一个循环是选择区分候选样本的外循环。第二个循环，内循环选择所选样本上的鉴别候选特征，以计算特定位置（例如视图/姿态）的所有弱分类器。然后将所有计算的弱分类器自动组合成最终分类器（强分类器），该分类器是要检测的对象。

10. 发明授权

US09053194B2 Method and apparatus for correlating and viewing disparate data 有权
标题翻译：用于关联和查看不同数据的方法和装置
公开(公告)号：US09053194B2
公开(公告)日：2015-06-09
申请号：US13484520
申请日：2012-05-31
申请人： Harpreet Singh Sawhney , Jayakrishnan Eledath , Ajay Divakaran , Mayank Bansal , Hui Cheng
发明人： Harpreet Singh Sawhney , Jayakrishnan Eledath , Ajay Divakaran , Mayank Bansal , Hui Cheng
IPC分类号： G06F17/30
CPC分类号： G06F17/2785 , G06F17/30047 , G06F17/30867 , G06F17/3087
摘要： A computer-implemented method comprising collecting data from a plurality of information sources, identifying a geographic location associated with the data and forming a corresponding event according to the geographic location, correlating the data and the event with one or more topics based at least partly on the identified geographic location and storing the correlated data and event and inferring the associated geographic location if the data does not comprise explicit location information, including matching the data against a database of geo-referenced data.
摘要翻译：一种计算机实现的方法，包括从多个信息源收集数据，识别与所述数据相关联的地理位置并根据所述地理位置形成对应的事件，至少部分地基于所述数据和所述事件与一个或多个主题相关联所识别的地理位置并存储相关数据和事件，并且如果数据不包括显式位置信息，则推断相关联的地理位置，包括将数据与地理参考数据的数据库进行匹配。

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