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1. 发明授权

US07929728B2 Method and apparatus for tracking a movable object 有权
标题翻译：用于跟踪可移动物体的方法和装置
公开(公告)号：US07929728B2
公开(公告)日：2011-04-19
申请号：US11295688
申请日：2005-12-05
申请人： Yanlin Guo , Harpreet Sawhney , Rakesh Kumar , Ying Shan , Steve Hsu
发明人： Yanlin Guo , Harpreet Sawhney , Rakesh Kumar , Ying Shan , Steve Hsu
IPC分类号： G06K9/00
CPC分类号： G06K9/3241 , G06K9/00711 , G06K2209/23 , G06T7/20 , G06T7/246 , G06T7/33
摘要： A method and apparatus for tracking a movable object using a plurality of images, each of which is separated by an interval of time is disclosed. The plurality of images includes first and second images. The method and apparatus include elements for aligning the first and second images as a function of (i) at least one feature of a first movable object captured in the first image, and (ii) at least one feature of a second movable object captured in the second image; and after aligning the first and second images, comparing at least one portion of the first image with at least one portion of the second image.
摘要翻译：公开了一种使用多个图像跟踪可移动物体的方法和装置，每个图像间隔一段时间。多个图像包括第一和第二图像。所述方法和装置包括用于将第一和第二图像对准的元件作为（i）在第一图像中捕获的第一可移动物体的至少一个特征的功能，以及（ii）捕获在第一图像中的第二可移动物体的至少一个特征第二个图像; 并且在对准所述第一和第二图像之后，将所述第一图像的至少一部分与所述第二图像的至少一部分进行比较。

2. 发明申请

US20060204035A1 Method and apparatus for tracking a movable object 有权
标题翻译：用于跟踪可移动物体的方法和装置
公开(公告)号：US20060204035A1
公开(公告)日：2006-09-14
申请号：US11295688
申请日：2005-12-05
申请人： Yanlin Guo , Harpreet Sawhney , Rakesh Kumar , Ying Shan , Steve Hsu
发明人： Yanlin Guo , Harpreet Sawhney , Rakesh Kumar , Ying Shan , Steve Hsu
IPC分类号： G06K9/00 , H04N5/225
CPC分类号： G06K9/3241 , G06K9/00711 , G06K2209/23 , G06T7/20 , G06T7/246 , G06T7/33
摘要： A method and apparatus for tracking a movable object using a plurality of images, each of which is separated by an interval of time is disclosed. The plurality of images includes first and second images. The method and apparatus include elements for aligning the first and second images as a function of (i) at least one feature of a first movable object captured in the first image, and (ii) at least one feature of a second movable object captured in the second image; and after aligning the first and second images, comparing at least one portion of the first image with at least one portion of the second image.
摘要翻译：公开了一种使用多个图像跟踪可移动物体的方法和装置，每个图像间隔一段时间。多个图像包括第一和第二图像。所述方法和装置包括用于将第一和第二图像对准的元件作为（i）在第一图像中捕获的第一可移动物体的至少一个特征的功能，以及（ii）捕获在第一图像中的第二可移动物体的至少一个特征第二个图像; 并且在对准所述第一和第二图像之后，将所述第一图像的至少一部分与所述第二图像的至少一部分进行比较。

3. 发明授权

US06353678B1 Method and apparatus for detecting independent motion in three-dimensional scenes 有权
标题翻译：用于检测立体场景独立运动的方法和装置
公开(公告)号：US06353678B1
公开(公告)日：2002-03-05
申请号：US09614690
申请日：2000-07-13
申请人： Yanlin Guo , Rakesh Kumar , Harpreet Sawhney
发明人： Yanlin Guo , Rakesh Kumar , Harpreet Sawhney
IPC分类号： G06K900
CPC分类号： G06K9/209 , G06T7/285
摘要： A system and method that detects independently moving objects in 3D scenes which are viewed under camera motion progressively applies constraints to the images to ensure the stability of the constraints. The system first calculates 2D view geometry constraints for a set of images. These constraints are tested to determine if the imaged scene exhibits significant 3D characteristics. If it does, then 3D shape constraints, are applied to the set of images. The 3D shape constraints are themselves constrained by the 2D view geometry constraints. The set of images is then tested to identify areas that are inconsistent with the 2D or 3D constraints. These areas correspond to the moving objects. The 2D view geometry constraints are calculated by computing a dominant image alignment for successive pairs of images and then computing constrained epipolar transformations for the two image pairs. This 2D view geometry is further refined based on a plurality of target point correspondences among the plurality of frames. The epipolar geometry for the point correspondence having a minimum median error is selected as the 2D view geometry of the scene. The 3D shape constraint is a parallax geometry that is calculated by iteratively minimizing errors in a parametric alignment of the images using an estimated parallax geometry.
摘要翻译：检测在相机运动中观看的3D场景中的独立运动物体的系统和方法逐渐对图像施加约束以确保约束的稳定性。系统首先计算一组图像的2D视图几何约束。测试这些约束以确定成像的场景是否显示出显着的3D特征。如果是，则3D形状约束被应用于图像集合。 3D形状约束本身受2D视图几何约束的限制。然后测试该组图像以识别与2D或3D约束不一致的区域。这些区域对应于移动物体。通过计算连续图像对的主要图像对准，然后计算两个图像对的受限的对极变换来计算2D视图几何约束。基于多个帧中的多个目标点对应关系进一步改进该2D视图几何。选择具有最小中值误差的点对应的对极几何作为场景的2D视图几何。 3D形状约束是通过使用估计的视差几何迭代地最小化图像的参数对齐中的误差来计算的视差几何。

4. 发明申请

US20060165277A1 Method and apparatus for unsupervised learning of discriminative edge measures for vehicle matching between non-overlapping cameras 有权
标题翻译：用于非重叠摄像机之间的车辆匹配的鉴别边缘测量的无监督学习的方法和装置
公开(公告)号：US20060165277A1
公开(公告)日：2006-07-27
申请号：US11295143
申请日：2005-12-05
申请人： Ying Shan , Rakesh Kumar , Harpreet Sawhney
发明人： Ying Shan , Rakesh Kumar , Harpreet Sawhney
IPC分类号： G06K9/62 , G06K9/00
CPC分类号： G06K9/6293 , G06K9/00785 , G06K9/3241 , G06K2209/23 , G06T7/33
摘要： A method and apparatus for unsupervised learning of measures for matching objects between images from at least two non-overlapping cameras is disclosed The method includes collecting at least one two pairs of feature maps, where the at least one two pairs of feature maps are derived from features of objects captured in the images. The method further includes computing, as a function of at least one two pairs of feature maps, at least one first and second match measures, wherein the first match measure is of a same class and the second match measure is of a different class.
摘要翻译：公开了一种用于无人监督学习用于在来自至少两个非重叠相机的图像之间匹配对象的措施的方法和装置。该方法包括收集至少一对两对特征图，其中至少一对两对特征图从在图像中捕获的对象的特征。所述方法还包括根据至少一对两对特征图来计算至少一个第一和第二匹配度量，其中所述第一匹配度量具有相同类别，并且所述第二匹配度量是不同类别的。

5. 发明授权

US07650030B2 Method and apparatus for unsupervised learning of discriminative edge measures for vehicle matching between non-overlapping cameras 有权
标题翻译：用于非重叠摄像机之间的车辆匹配的鉴别边缘测量的无监督学习的方法和装置
公开(公告)号：US07650030B2
公开(公告)日：2010-01-19
申请号：US11295143
申请日：2005-12-05
申请人： Ying Shan , Rakesh Kumar , Harpreet Sawhney
发明人： Ying Shan , Rakesh Kumar , Harpreet Sawhney
IPC分类号： G06K9/62
CPC分类号： G06K9/6293 , G06K9/00785 , G06K9/3241 , G06K2209/23 , G06T7/33
摘要： A method and apparatus for unsupervised learning of measures for matching objects between images from at least two non-overlapping cameras is disclosed The method includes collecting at least two pairs of feature maps, where the at least two pairs of feature maps are derived from features of objects captured in the images. The method further includes computing, as a function of at least two pairs of feature maps, at least one first and second match measures, wherein the first match measure is of a same class and the second match measure is of a different class.
摘要翻译：公开了一种用于无人监督学习用于在来自至少两个非重叠相机的图像之间匹配对象的措施的方法和装置。所述方法包括收集至少两对特征图，其中所述至少两对特征图从在图像中捕获的对象。该方法还包括作为至少两对特征图的函数来计算至少一个第一匹配度量和第二匹配度量，其中所述第一匹配度量具有相同类别，并且所述第二匹配度量是不同类别的。

6. 发明申请

US20080025568A1 SYSTEM AND METHOD FOR DETECTING STILL OBJECTS IN IMAGES 有权
标题翻译：用于检测图像中的静态对象的系统和方法
公开(公告)号：US20080025568A1
公开(公告)日：2008-01-31
申请号：US11780109
申请日：2007-07-19
申请人： Feng Han , Ying Shan , Ryan Cekander , Harpreet Sawhney , Rakesh Kumar
发明人： Feng Han , Ying Shan , Ryan Cekander , Harpreet Sawhney , Rakesh Kumar
IPC分类号： G06K9/00
CPC分类号： G06K9/4642
摘要： The present invention provides an improved system and method for object detection with histogram of oriented gradient (HOG) based support vector machine (SVM). Specifically, the system provides a computational framework to stably detect still or not moving objects over a wide range of viewpoints. The framework includes providing a sensor input of images which are received by the “focus of attention” mechanism to identify the regions in the image that potentially contain the target objects. These regions are further computed to generate hypothesized objects, specifically generating selected regions containing the target object hypothesis with respect to their positions. Thereafter, these selected regions are verified by an extended HOG-based SVM classifier to generate the detected objects.
摘要翻译：本发明提供一种用于基于定向梯度（HOG）的支持向量机（SVM）直方图的物体检测的改进的系统和方法。具体地，该系统提供了一个计算框架，用于在宽范围的视点中稳定地检测静止或不移动的物体。框架包括提供通过“注意力”机制接收的图像的传感器输入，以识别可能包含目标对象的图像中的区域。进一步计算这些区域以产生假设对象，特别地生成包含关于其位置的目标对象假设的选定区域。此后，通过扩展的基于HOG的SVM分类器验证这些选择的区域以生成检测到的对象。

7. 发明授权

US07519197B2 Object identification between non-overlapping cameras without direct feature matching 有权
标题翻译：非重叠相机之间的物体识别，无需直接特征匹配
公开(公告)号：US07519197B2
公开(公告)日：2009-04-14
申请号：US11393062
申请日：2006-03-30
申请人： Ying Shan , Harpreet Sawhney , Rakesh Kumar
发明人： Ying Shan , Harpreet Sawhney , Rakesh Kumar
IPC分类号： G06K9/00 , G06K9/62
CPC分类号： G06K9/3241 , G06K9/00791 , G06K9/6211 , G06K2209/23
摘要： A system and method for identifying objects, particularly vehicles, between two non-overlapping cameras. More specifically, the method and system determines whether a vehicle depicted in an image captured by a first camera is the same vehicle or a different vehicle than a vehicle depicted in an image captured by a second camera. This intra-camera analysis determines whether the vehicle viewed by the first camera is the same as the vehicle viewed by the second camera, without directly matching the two vehicle images, thus eliminating the problems and inaccuracies caused by disparate environmental conditions acting on the two cameras, such as dramatic appearance and aspect changes.
摘要翻译：用于识别两个非重叠相机之间的对象，特别是车辆的系统和方法。更具体地，该方法和系统确定由第一照相机拍摄的图像中描绘的车辆是否与由第二相机拍摄的图像中描绘的车辆相同的车辆或不同的车辆。这种相机内分析确定第一相机观看的车辆是否与由第二相机观看的车辆相同，而不直接匹配两个车辆图像，从而消除了由两个相机作用的不同环境条件引起的问题和不准确，如戏剧性的外观和方面的变化。

8. 发明申请

US20060245617A1 Object identification between non-overlapping cameras without direct feature matching 有权
公开(公告)号：US20060245617A1
公开(公告)日：2006-11-02
申请号：US11393062
申请日：2006-03-30
申请人： Ying Shan , Harpreet Sawhney , Rakesh Kumar
发明人： Ying Shan , Harpreet Sawhney , Rakesh Kumar
IPC分类号： G06K9/00
CPC分类号： G06K9/3241 , G06K9/00791 , G06K9/6211 , G06K2209/23
摘要： A system and method for identifying objects, particularly vehicles, between two non-overlapping cameras. More specifically, the method and system determines whether a vehicle depicted in an image captured by a first camera is the same vehicle or a different vehicle than a vehicle depicted in an image captured by a second camera. This intra-camera analysis determines whether the vehicle viewed by the first camera is the same as the vehicle viewed by the second camera, without directly matching the two vehicle images, thus eliminating the problems and inaccuracies caused by disparate environmental conditions acting on the two cameras, such as dramatic appearance and aspect changes.

9. 发明申请

US20060013450A1 Method and apparatus for recognizing 3-D objects 有权
公开(公告)号：US20060013450A1
公开(公告)日：2006-01-19
申请号：US11159969
申请日：2005-06-22
申请人： Ying Shan , Bogdan Mihai Matei , Harpreet Sawhney , Rakesh Kumar
发明人： Ying Shan , Bogdan Mihai Matei , Harpreet Sawhney , Rakesh Kumar
IPC分类号： G06K9/00
CPC分类号： G06K9/00201 , G06K9/00214 , G06K9/6211
摘要： A method and apparatus for recognizing an object, comprising providing a set of scene features from a scene, pruning a set of model features, generating a set of hypotheses associated with the pruned set of model features for the set of scene features, pruning the set of hypotheses, and verifying the set of pruned hypotheses is provided.

10. 发明申请

US20080089579A1 SYSTEM AND METHOD FOR DETECTION OF MULTI-VIEW/MULTI-POSE OBJECTS 有权
标题翻译：用于检测多视图/多位置对象的系统和方法
公开(公告)号：US20080089579A1
公开(公告)日：2008-04-17
申请号：US11762400
申请日：2007-06-13
申请人： Feng Han , Ying Shan , Harpreet Sawhney , Rakesh Kumar
发明人： Feng Han , Ying Shan , Harpreet 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。

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