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公开(公告)号：US20150356352A1
公开(公告)日：2015-12-10
申请号：US14823392
申请日：2015-08-11
发明人： Rogerio S. Feris , Yun Zhai
CPC分类号： G06K9/00711 , G06K9/00744 , G06K9/00771 , G06K9/4604 , G06K9/4652 , G06T5/50 , G06T7/11 , G06T7/174 , G06T7/194 , G06T7/75 , G06T2207/10016 , G06T2207/10024 , G06T2207/20024 , G06T2207/20216
摘要： Long-term understanding of background modeling includes determining first and second dimension gradient model derivatives of image brightness data of an image pixel along respective dimensions of two-dimensional, single channel image brightness data of a static image scene. The determined gradients are averaged with previous determined gradients of the image pixels, and with gradients of neighboring pixels as a function of their respective distances to the image pixel, the averaging generating averaged pixel gradient models for each of a plurality of pixels of the video image data of the static image scene that each have mean values and weight values. Background models for the static image scene are constructed as a function of the averaged pixel gradients and weights, wherein the background model pixels are represented by averaged pixel gradient models having similar orientation and magnitude and weights meeting a threshold weight requirement.
摘要翻译： 对背景建模的长期理解包括确定静态图像场景的二维单通道图像亮度数据的各个维度上的图像像素的图像亮度数据的第一和第二维度梯度模型导数。 所确定的梯度与先前确定的图像像素的梯度平均，并且随着相邻像素的梯度作为它们到图像像素的相应距离的函数，平均生成视频图像的多个像素中的每个像素的平均像素梯度模型 静态图像场景的数据各自具有平均值和权重值。 静态图像场景的背景模型被构造为平均像素梯度和权重的函数，其中背景模型像素由具有相似取向和幅度以及满足阈值权重要求的权重的平均像素梯度模型表示。
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公开(公告)号：US20150324368A1
公开(公告)日：2015-11-12
申请号：US14803485
申请日：2015-07-20
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 discernable 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 as a function of relevance of extracted features to attributes relevant to the estimated regions, and in second-layer rankings as a function of combinations of the base layer rankings and relevance of the extracted features to common ones of the attributes relevant to the estimated regions. The images are ranked in relevance to the query as a function of 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。 从估计的面部区域从数据库面部图像提取不同的特征，数据库面部图像作为基于提取的特征与与估计区域相关的属性的相关性的函数被排列在基本层排名中，并且在第二层排名中作为组合的函数 基本层的排名和提取的特征与与估计区域相关的常规属性的相关性。 作为第二层次排序的函数，图像与查询相关。
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公开(公告)号：US20150310631A1
公开(公告)日：2015-10-29
申请号：US14735705
申请日：2015-06-10
发明人： Rogerio S. Feris , Arun Hampapur , Zouxuan Lu , Ying-li Tian
CPC分类号： G06T7/2053 , G06K9/00711 , G06K9/00718 , G06K9/00771 , G06K9/44 , G06T7/187 , G06T7/194 , G06T7/254 , G06T2207/10016 , G06T2207/10024 , G06T2207/20036 , G06T2207/30232
摘要： A method and system for real time processing of a sequence of video frames. A current frame in the sequence and at least one frame in the sequence occurring prior to the current frame is analyzed. The sequence of video frames is received in synchronization with a recording of the video frames in real time. The analyzing includes performing a background subtraction on the at least one frame, which determines a background image and a static region mask associated with a static region consisting of a contiguous distribution of pixels in the current frame, which includes executing a mixture of 3 to 5 Gaussians algorithm coupled together in a linear combination by Gaussian weight coefficients to generate the background model, a foreground image, and the static region. The static region mask identifies each pixel in the static region upon the static region mask being superimposed on the current frame.
摘要翻译： 一种用于实时处理视频帧序列的方法和系统。 分析序列中的当前帧和在当前帧之前发生的序列中的至少一个帧。 视频帧的序列与视频帧的实时记录同步地被接收。 所述分析包括在所述至少一个帧上执行背景减除，其确定与由当前帧中的连续的像素分布组成的静态区域相关联的背景图像和静态区域掩模，所述静态区域包括执行3至5的混合 高斯算法通过高斯加权系数以线性组合耦合在一起，以生成背景模型，前景图像和静态区域。 当静态区域掩模叠加在当前帧上时，静态区域掩模识别静态区域中的每个像素。
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公开(公告)号：US09171375B2
公开(公告)日：2015-10-27
申请号：US14538233
申请日：2014-11-11
CPC分类号： G06T7/11 , G06K9/00536 , G06K9/00785 , G06K9/3241 , G06K9/38 , G06K9/4642 , G06T7/13 , G06T7/194 , G06T2207/10016 , G06T2207/10024 , G06T2207/20021 , G06T2207/30236
摘要： Foreground objects of interest are distinguished from a background model by dividing a region of interest of a video data image into a grid array of individual cells. Each of the cells are labeled as foreground if accumulated edge energy within the cell meets an edge energy threshold, or if color intensities for different colors within each cell differ by a color intensity differential threshold, or as a function of combinations of said determinations.
摘要翻译： 将感兴趣的前景物体与背景模型区分开，将视频数据图像的感兴趣区域划分为单个单元格的网格阵列。 如果单元内的累积边缘能量满足边缘能量阈值，或者如果每个单元内的不同颜色的颜色强度相差颜色强度差异阈值，或者作为所述确定的组合的函数，则每个单元被标记为前景。
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公开(公告)号：US09158972B2
公开(公告)日：2015-10-13
申请号：US14478242
申请日：2014-09-05
发明人： Ankur Datta , Rogerio S. Feris , Yun Zhai
CPC分类号： G06T17/10 , G06K9/00711 , G06K9/00771 , G06K9/46 , G06K9/4604 , G06K9/52 , G06K2009/4666 , G06T3/20 , G06T3/40 , G06T7/20 , G06T7/251 , G06T7/62 , G06T7/75 , G06T15/205 , G06T19/20 , G06T2200/24 , G06T2207/10016 , G06T2207/30232 , G06T2207/30236 , G06T2219/2004 , G06T2219/2016 , H04N13/264 , H04N13/293 , H04N2013/0088
摘要： Objects within two-dimensional video data are modeled by three-dimensional models as a function of object type and motion through manually calibrating a two-dimensional image to the three spatial dimensions of a three-dimensional modeling cube. Calibrated three-dimensional locations of an object in motion in the two-dimensional image field of view of a video data input are determined and used to determine a heading direction of the object as a function of the camera calibration and determined movement between the determined three-dimensional locations. The two-dimensional object image is replaced in the video data input with an object-type three-dimensional polygonal model having a projected bounding box that best matches a bounding box of an image blob, the model oriented in the determined heading direction. The bounding box of the replacing model is then scaled to fit the object image blob bounding box, and rendered with extracted image features.
摘要翻译： 通过将二维图像手动校准到三维建模立方体的三个空间维度，二维视频数据内的对象由作为对象类型和运动的函数的三维模型建模。 确定在视频数据输入的二维图像视场中运动的物体的校准三维位置，并用于根据相机校准确定物体的航向方向，并确定所确定的三个 维度位置。 在视频数据输入中，二维对象图像被替换为具有与图像斑块的边界框最佳匹配的投影边界框的对象型三维多边形模型，模型在确定的方位方向上取向。 然后将替换模型的边界框缩放以适合对象图像Blob边界框，并使用提取的图像特征进行渲染。
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公开(公告)号：US20150287213A1
公开(公告)日：2015-10-08
申请号：US14735455
申请日：2015-06-10
发明人： Rogerio S. Feris , Arun Hampapur , Zouxuan Lu , Ying-li Tian
CPC分类号： G06T7/2053 , G06K9/00711 , G06K9/00718 , G06K9/00771 , G06K9/44 , G06T7/187 , G06T7/194 , G06T7/254 , G06T2207/10016 , G06T2207/10024 , G06T2207/20036 , G06T2207/30232
摘要： A method and system for real time processing of a sequence of video frames. A current frame in the sequence and at least one frame in the sequence occurring prior to the current frame is analyzed. Each frame includes a two-dimensional array of pixels. The sequence of video frames is received in synchronization with a recording of the video frames in real time. The analyzing includes performing a background subtraction on the at least one frame, which determines a background image and a static region mask associated with a static region consisting of a contiguous distribution of pixels in the current frame. The static region mask identifies each pixel in the static region upon the static region mask being superimposed on the current frame. A determination is made that a persistence requirement, both a non-persistence duration requirement and a persistence duration requirement, or a combination thereof have been satisfied.
摘要翻译： 一种用于实时处理视频帧序列的方法和系统。 分析序列中的当前帧和在当前帧之前发生的序列中的至少一个帧。 每帧包括二维像素阵列。 视频帧的序列与视频帧的实时记录同步地被接收。 所述分析包括在所述至少一个帧上执行背景减除，其确定与由当前帧中的连续的像素分布组成的静态区域相关联的背景图像和静态区域掩模。 当静态区域掩模叠加在当前帧上时，静态区域掩模识别静态区域中的每个像素。 确定已经满足持久性要求（非持久持续时间要求和持续持续时间要求）或其组合。
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公开(公告)号：US20150063689A1
公开(公告)日：2015-03-05
申请号：US14538233
申请日：2014-11-11
IPC分类号： G06T7/00
CPC分类号： G06T7/11 , G06K9/00536 , G06K9/00785 , G06K9/3241 , G06K9/38 , G06K9/4642 , G06T7/13 , G06T7/194 , G06T2207/10016 , G06T2207/10024 , G06T2207/20021 , G06T2207/30236
摘要： Foreground objects of interest are distinguished from a background model by dividing a region of interest of a video data image into a grid array of individual cells. Each of the cells are labeled as foreground if accumulated edge energy within the cell meets an edge energy threshold, or if color intensities for different colors within each cell differ by a color intensity differential threshold, or as a function of combinations of said determinations.
摘要翻译： 将感兴趣的前景物体与背景模型区分开，将视频数据图像的感兴趣区域划分为单个单元格的网格阵列。 如果单元内的累积边缘能量满足边缘能量阈值，或者如果每个单元内的不同颜色的颜色强度相差颜色强度差异阈值，或者作为所述确定的组合的函数，则每个单元被标记为前景。
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公开(公告)号：US08948454B2
公开(公告)日：2015-02-03
申请号：US13732471
申请日：2013-01-02
CPC分类号： G06K9/6256 , G06K9/6259 , G06K9/6262
摘要： A method and system for training a special object detector to distinguish a foreground object appearing in a sequence of frames for a target domain. The sequence of frames depicts motion of the foreground object in a non-uniform background. The foreground object is detected in a high-confidence subwindow of an initial frame of the sequence, which includes computing a measure of confidence that the high-confidence subwindow includes the foreground object and determining that the measure of confidence exceeds a specified confidence threshold. The foreground object is tracked in respective positive subwindows of subsequent frames appearing after the initial frame. The subsequent frames are within a specified short period of time. The positive subwindows are used to train the special object detector to detect the foreground object in the target domain. The positive subwindows include the subwindow of the initial frame and the respective subwindows of the subsequent frames.
摘要翻译： 一种用于训练特殊对象检测器以区分出现在目标域的帧序列中的前景对象的方法和系统。 帧序列描绘了前景对象在非均匀背景中的运动。 在序列的初始帧的高可信度子窗口中检测前景对象，其包括计算高置信度子窗口包括前景对象并确定置信度量度超过指定的置信阈值的置信度度量。 在初始帧之后出现的后续帧的相应正向子窗中跟踪前景对象。 随后的帧在指定的短时间内。 正向子窗口用于训练特殊对象检测器以检测目标域中的前景对象。 正向子窗口包括初始帧的子窗口和后续帧的相应子窗口。
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公开(公告)号：US20140153779A1
公开(公告)日：2014-06-05
申请号：US14175335
申请日：2014-02-07
IPC分类号： G06T7/00
CPC分类号： G06T7/0079 , G06K9/34 , G06T7/11 , G06T7/136 , G06T2207/10024 , G06T2207/20012 , G06T2207/20021
摘要： Techniques for segmenting an object are provided. The techniques include capturing an image of an object, dividing the image into one or more blocks, computing a confidence value for each of the one or more blocks, and eliminating one or more blocks from consideration based on the confidence value for each of the one or more blocks.
摘要翻译： 提供分割对象的技术。 这些技术包括捕获对象的图像，将图像划分成一个或多个块，计算一个或多个块中的每个块的置信度值，以及基于每个块中的每一个的置信度值来排除考虑中的一个或多个块 或更多块。
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公开(公告)号：US08744176B2
公开(公告)日：2014-06-03
申请号：US13864598
申请日：2013-04-17
CPC分类号： G06T7/0079 , G06K9/34 , G06T7/11 , G06T7/136 , G06T2207/10024 , G06T2207/20012 , G06T2207/20021
摘要： Techniques for segmenting an object at a self-checkout are provided. The techniques include capturing an image of an object at a self-checkout, dividing the image into one or more blocks, computing a confidence value for each of the one or more blocks, and eliminating one or more blocks from consideration based on the confidence value for each of the one or more blocks, wherein the one or more blocks remaining map to a region of the image containing the object.
摘要翻译： 提供了在自检结果中分割对象的技术。 这些技术包括在自检结果中捕获对象的图像，将图像划分成一个或多个块，计算一个或多个块中的每个块的置信度值，以及基于置信度值来排除考虑中的一个或多个块 对于所述一个或多个块中的每个块，其中所述一个或多个块保持映射到包含所述对象的图像的区域。
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