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公开(公告)号：US20170046587A1
公开(公告)日：2017-02-16
申请号：US14962791
申请日：2015-12-08
发明人： Russell P. Bobbitt , Rogerio S. Feris , Chiao-Fe Shu , Yun Zhai
CPC分类号： G06K9/46 , G06K9/00771 , G06K9/626 , G06K2009/4666 , G06K2209/27 , H04N7/181
摘要： Embodiments are directed to an object detection system having at least one processor circuit configured to receive a series of image regions and apply to each image region in the series a detector, which is configured to determine a presence of a predetermined object in the image region. The object detection system performs a method of selecting and applying the detector from among a plurality of foreground detectors and a plurality of background detectors in a repeated pattern that includes sequentially selecting a selected one of the plurality of foreground detectors; sequentially applying the selected one of the plurality of foreground detectors to one of the series of image regions until all of the plurality of foreground detectors have been applied; selecting a selected one of the plurality of background detectors; and applying the selected one of the plurality of background detectors to one of the series of image regions.
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72.发明授权公开(公告)号：US09495599B2
公开(公告)日：2016-11-15
申请号：US14711871
申请日：2015-05-14
发明人： Russell P. Bobbitt , Rogerio S. Feris , Yun Zhai
CPC分类号： G06K9/00771 , G06K9/00718 , G06K9/00765 , G06K9/6212 , G06T7/11
摘要： Foreground feature data and motion feature data is determined for frames of video data acquired from a train track area region of interest. The frames are labeled as “train present” if the determined foreground feature data value meets a threshold value, else as “train absent; and as “motion present” if the motion feature data meets a motion threshold, else as “static.” The labels are used to classify segments of the video data comprising groups of consecutive video frames, namely as within a “no train present” segment for groups with “train absent” and “static” labels; within a “train present and in transition” segment for groups “train present” and “motion present” labels; and within a “train present and stopped” segment for groups with “train present” and “static” labels. The presence or motion state of a train at a time of inquiry is thereby determined from the respective segment classification.
摘要翻译： 确定从感兴趣的列车轨道区域获取的视频数据的帧的前景特征数据和运动特征数据。 如果确定的前景特征数据值满足阈值，则帧被标记为“列车存在”，否则被标记为“列车存在” 并且如果运动特征数据满足运动阈值，则作为“运动呈现”，否则为“静态”。标签用于对包括连续视频帧组的视频数据的段进行分类，即在“无列车存在”段内 对于具有“火车不在”和“静态”标签的组; 在“火车现在”和“现场演出”标签的“火车现在和转型期”段内， 在“火车现在”和“静态”标签的组别内的“火车现在和停止”部分。 因此，从相应的段分类确定列车在询问时的存在或运动状态。
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公开(公告)号：US09477890B2
公开(公告)日：2016-10-25
申请号：US14073420
申请日：2013-11-06
发明人： Lisa M. Brown , Rogerio S. Feris
IPC分类号： G06K9/00
CPC分类号： G06K9/00771
摘要： Techniques for object detection are provided that employ limited learned attribute ranges. One or more objects are initially detected for a full range of one or more attributes at each location of an image. Thereafter, a set of positional constraints are generated indicating an expected range of values for each position in the image for one or more of the attributes based on the detected objects employing a geometric model of a scene in the image. Objects are then detected in the image using the expected range of values for each position in the image for the one or more the attributes. The attributes comprise, for example, one or more of size, pose and rotation of the objects. A best fit is computed to the geometric model to generate the set of positional constraints, for example, using a least squares approach.
摘要翻译： 提供了使用有限的学习属性范围的对象检测技术。 对于图像的每个位置处的一个或多个属性的完整范围，最初检测到一个或多个对象。 此后，基于使用图像中的场景的几何模型的检测对象，生成一组位置约束，指示图像中的一个或多个属性的每个位置的值的期望范围。 然后在图像中使用针对一个或多个属性的图像中的每个位置的值的期望范围来检测对象。 属性包括例如对象的大小，姿态和旋转中的一个或多个。 对几何模型计算最佳拟合以产生一组位置约束，例如使用最小二乘法。
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公开(公告)号：US09424659B2
公开(公告)日：2016-08-23
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：US09418444B2
公开(公告)日：2016-08-16
申请号：US14735568
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. The static region mask identifies each pixel in the static region upon the static region mask being superimposed on the current frame. A status of a static object is determined as either an abandoned status if the static object is an abandoned object or a removed status if the static object is a removed object.
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公开(公告)号：US20160124996A1
公开(公告)日：2016-05-05
申请号：US14994501
申请日：2016-01-13
CPC分类号： G06F17/30256 , G06F17/30277 , G06F17/3028 , G06F17/3053 , G06K9/00268 , G06K9/481 , G06K9/6263 , G06K9/66 , G06N99/005
摘要： Images are retrieved and ranked according to relevance to attributes of a multi-attribute query through training image attribute detectors for different attributes annotated in a training dataset. Pair-wise correlations are learned between pairs of the annotated attributes from the training dataset of images. Image datasets may are searched via the trained attribute detectors for images comprising attributes in a multi-attribute query. The retrieved images are ranked as a function of comprising attributes that are not within the query subset plurality of attributes but are paired to one of the query subset plurality of attributes by the pair-wise correlations, wherein the ranking is an order of likelihood that the different ones of the attributes will appear in an image with the paired one of the query subset plurality of attributes.
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公开(公告)号：US09251425B2
公开(公告)日：2016-02-02
申请号：US14620510
申请日：2015-02-12
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.
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公开(公告)号：US20150379768A1
公开(公告)日：2015-12-31
申请号：US14848773
申请日：2015-09-09
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：US09224049B2
公开(公告)日：2015-12-29
申请号：US14639429
申请日：2015-03-05
发明人： Rogerio S. Feris , Yun Zhai
CPC分类号： G06K9/00771 , G06K9/00805 , G06T7/194 , G06T7/254 , G06T7/269 , G06T2207/10016 , G06T2207/30232 , G06T2207/30236 , G06T2207/30241
摘要： Foreground object image features are extracted from input video via application of a background subtraction mask, and optical flow image features from a region of the input video image data defined by the extracted foreground object image features. If estimated movement features indicate that the underlying object is in motion, a dominant moving direction of the underlying object is determined. If the dominant moving direction is parallel to an orientation of the second, crossed thoroughfare, an event alarm indicating that a static object is blocking travel on the crossing second thoroughfare is not generated. If the estimated movement features indicate that the underlying object is static, or that its determined dominant moving direction is not parallel to the second thoroughfare, an appearance of the foreground object region is determined and a static-ness timer run while the foreground object region comprises the extracted foreground object image features.
摘要翻译： 通过应用背景减影掩模从输入视频提取前景对象图像特征，以及从提取的前景对象图像特征定义的输入视频图像数据的区域中的光流图像特征。 如果估计运动特征表明底层对象处于运动状态，则确定底层物体的主要移动方向。 如果主导移动方向平行于第二条交叉通道的方向，则不会产生指示静态物体在交叉第二条通道上阻挡行驶的事件警报。 如果估计的运动特征指示下面的对象是静态的，或者其确定的主要移动方向不与第二通道平行，则确定前景对象区域的外观，并且静态定时器在前景对象区域包括 提取的前景对象图像特征。
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80.发明授权Multi-view object detection using appearance model transfer from similar scenes 有权使用类似场景的外观模型传输的多视图对象检测公开(公告)号：US09224046B2
公开(公告)日：2015-12-29
申请号：US14599616
申请日：2015-01-19
CPC分类号： G06K9/00718 , G06K9/00771 , G06K9/00785 , G06K9/6218 , G06T7/246 , G06T7/73 , G06T2207/10016 , G06T2207/20081
摘要： View-specific object detectors are learned as a function of scene geometry and object motion patterns. Motion directions are determined for object images extracted from a training dataset and collected from different camera scene viewpoints. The object images are categorized into clusters as a function of similarities of their determined motion directions, the object images in each cluster are acquired from the same camera scene viewpoint. Zenith angles are estimated for object image poses in the clusters relative to a position of a horizon in the cluster camera scene viewpoint, and azimuth angles of the poses as a function of a relation of the determined motion directions of the clustered images to the cluster camera scene viewpoint. Detectors are thus built for recognizing objects in input video, one for each of the clusters, and associated with the estimated zenith angles and azimuth angles of the poses of the respective clusters.
摘要翻译： 视图特定对象检测器被学习为场景几何和对象运动模式的函数。 对从训练数据集提取并从不同的摄像机场景观点收集的对象图像确定运动方向。 对象图像根据其确定的运动方向的相似度被分类为群集，每个群集中的对象图像从相同的摄像机场景中获取。 针对群集相机场景中相对于水平位置的对象图像姿态估计天顶角度，以及作为所确定的聚类图像的运动方向与群集照相机的关系的姿态的方位角 现场观点 因此，检测器被构建用于识别输入视频中的对象，每个群集中的对象，并且与相应群集的姿势的估计的天顶角和方位角相关联。
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