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公开(公告)号：US11024041B2
公开(公告)日：2021-06-01
申请号：US16215348
申请日：2018-12-10
Applicant: Intel Corporation
Inventor： Koba Natroshvili , Kay-Ulrich Scholl
Abstract: A mechanism is described for facilitating depth and motion estimation in machine learning environments, according to one embodiment. A method of embodiments, as described herein, includes receiving a frame associated with a scene captured by one or more cameras of a computing device; processing the frame using a deep recurrent neural network architecture, wherein processing includes simultaneously predicating values associated with multiple loss functions corresponding to the frame; and estimating depth and motion based the predicted values.
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公开(公告)号：US10922817B2
公开(公告)日：2021-02-16
申请号：US16285253
申请日：2019-02-26
Applicant: Intel Corporation
Inventor： Koba Natroshvili
Abstract: A perception device, including at least one image sensor configured to detect a plurality of images; an information estimator configured to estimate from each image of the plurality of images a depth estimate, a velocity estimate, an object classification estimate and an odometry estimate; a particle generator configured to generate a plurality of particles, wherein each particle of the plurality of particles comprises a position value determined from the depth estimate, a velocity value determined from the velocity estimate and a classification value determined from the classification estimate; an occupancy hypothesis determiner configured to determine an occupancy hypothesis of a predetermined region, wherein each particle of the plurality of particles contributes to the determination of the occupancy hypothesis.
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公开(公告)号：US09437001B2
公开(公告)日：2016-09-06
申请号：US14490369
申请日：2014-09-18
Applicant: Intel Corporation
Inventor： Kay-Ulrich Scholl , Koba Natroshvili
CPC classification number: G06T7/0042 , B60R1/00 , B60R2300/10 , B60R2300/30 , B60R2300/303 , B60R2300/80 , G06K9/00805 , G06T3/0018 , G06T3/0062 , G06T7/536 , G06T2207/30252 , H04N5/23238
Abstract: Technologies for determining a distance of an object from a vehicle include a computing device to identify an object captured in a fisheye image generated by a fisheye camera of the vehicle. The computing device projects a contour of the identified object on a selected virtual plane that is located outside the vehicle and selected from a predefined set of virtual planes based on a location of the identified object relative to the vehicle. The computing device identifies a bottom of the projected contour on the selected virtual plane and determines an intersection point of an imaginary line with a ground plane coincident with a plane on which the vehicle is positioned. The imaginary line passes through each of the identified bottom of the projected contour and the fisheye camera. The computing device determines a location of the identified object relative to the vehicle based on the determined intersection point and the identified bottom of the projected contour.
Abstract translation: 用于确定物体与车辆的距离的技术包括用于识别由车辆的鱼眼照相机生成的鱼眼图像中捕获的物体的计算装置。 所述计算设备在位于车辆外部的所选择的虚拟平面上投射所识别对象的轮廓，并且基于所识别的对象相对于所述车辆的位置从预定义的一组虚拟平面中选择。 计算装置识别所选择的虚拟平面上的投影轮廓的底部，并且确定假想线与与车辆所在的平面重合的接地平面的交点。 假想线穿过所识别的投影轮廓的底部和鱼眼相机。 计算设备基于所确定的交叉点和所识别的投影轮廓的底部来确定所识别的对象相对于车辆的位置。
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公开(公告)号：US11798271B2
公开(公告)日：2023-10-24
申请号：US17330906
申请日：2021-05-26
Applicant: Intel Corporation
Inventor： Koba Natroshvili , Kay-Ulrich Scholl
IPC: G06T7/50 , G06V10/82 , G06T7/246 , G06N3/084 , G06V10/44 , G06F18/25 , G06F18/2413 , G06N3/044 , G06N3/045 , G06N3/048 , G06V10/764 , G06V10/80
CPC classification number: G06V10/82 , G06F18/2413 , G06F18/25 , G06N3/044 , G06N3/045 , G06N3/048 , G06N3/084 , G06T7/251 , G06T7/50 , G06V10/454 , G06V10/764 , G06V10/80 , G06T2207/20084
Abstract: A mechanism is described for facilitating depth and motion estimation in machine learning environments, according to one embodiment. A method of embodiments, as described herein, includes receiving a frame associated with a scene captured by one or more cameras of a computing device; processing the frame using a deep recurrent neural network architecture, wherein processing includes simultaneously predicating values associated with multiple loss functions corresponding to the frame; and estimating depth and motion based the predicted values.
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公开(公告)号：US11040714B2
公开(公告)日：2021-06-22
申请号：US16245438
申请日：2019-01-11
Applicant: Intel Corporation
Inventor： Koba Natroshvili , Kay-Ulrich Scholl
Abstract: According to various examples, a vehicle controller is described comprising a determiner configured to determine information about surroundings of a vehicle, the information about the surroundings comprising information about velocities of objects in the surroundings of the vehicle and a velocity controller configured to input the information about the surroundings of the vehicle and a specification of a path of the vehicle to a convolutional neural network, to determine a target velocity of the vehicle along the path based on an output of the convolutional neural network and to control the vehicle according to the determined target velocity.
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Patent Agency Ranking
公开(公告)号：US10754032B2
公开(公告)日：2020-08-25
申请号：US16014013
申请日：2018-06-21
Applicant: Intel Corporation
Inventor： Koba Natroshvili , Klaus Uhl
IPC: G01S17/04 , G01S19/42 , B60W40/10 , B60W40/04 , G01C21/28 , G01S7/48 , G01S17/89 , G01S17/931 , G01S5/16 , G05D1/02 , G01C21/10 , B60N2/853
Abstract: A perception device including a receiver configured to receive sensor information including information about a location of one or more objects detected by a sensor; a memory configured to store an occupancy grid of a predetermined region, wherein the occupancy grid includes a plurality of grid cells, wherein each grid cell represents an area in the predetermined region, wherein at least one grid cell of the plurality of grid cells is associated with a respective single occupancy hypothesis; a single occupancy hypothesis determiner configured to determine a single occupancy hypothesis; wherein the single occupancy hypothesis includes degree of belief of occupancy of the grid cell depending on the sensor information; wherein a contribution of a sensor information value to the degree of belief of occupancy substantially decreases with an increase of a distance of the location of the object detected by the sensor from a center of the grid cell.
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公开(公告)号：US20190126922A1
公开(公告)日：2019-05-02
申请号：US16233164
申请日：2018-12-27
Applicant: Intel Corporation
Inventor： Koba Natroshvili
IPC: B60W30/095 , G06K9/00 , G06T7/20 , G06T7/11
Abstract: A method of determining a trajectory of motion of a vehicle in a predetermined region, wherein the predetermined region includes a plurality of sub-regions, the method being executed by one or more processors, the method including determining an occupancy hypothesis of the predetermined region, wherein the occupancy hypothesis indicates occupied sub-regions of the plurality of sub-regions and non-occupied sub-regions of the plurality of sub-regions; determining a utility value for each sub-region of the predetermined region; determining the trajectory of motion which crosses at least one sub-region of the non-occupied sub-regions, based on a function of the utility values of the least one sub-region of the non-occupied sub-regions crossed by the trajectory of motion and by maximizing a utility of motion of the vehicle, wherein the utility of motion of the vehicle is indicated by a function of the utility values of the sub-regions crossed by the trajectory of motion.
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公开(公告)号：US09659234B1
公开(公告)日：2017-05-23
申请号：US14955476
申请日：2015-12-01
Applicant: INTEL CORPORATION
Inventor： Talat Anand Mohan Kalyanasundaram , Koba Natroshvili
CPC classification number: G06K9/623 , G06K9/4671 , G06K9/6211 , G06T5/20 , G06T5/50 , G06T7/0085 , G06T7/50 , G06T2207/20024 , G06T2207/20076
Abstract: Techniques are provided for adaptive selection of feature keypoints of an image. An example system may include a contrast statistics calculation circuit configured to generate contrast measurements of regions of the image associated with each of the feature keypoints, and to calculate a mean and variance of the contrast measurements. The system may also include an edge statistics calculation circuit configured to generate ratios of principal curvatures of regions of the image associated with each of the feature keypoints, and to calculate a mean and variance of the ratios of principal curvatures. The system may further include a threshold calculation circuit configured to calculate thresholds based on the mean and variance of the contrast measurements and on the mean and variance of the ratios of principal curvatures; and a keypoint filter circuit configured to filter the set of feature keypoints based on the those thresholds.
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9.发明申请公开(公告)号：US20160358100A1
公开(公告)日：2016-12-08
申请号：US14731479
申请日：2015-06-05
Applicant: INTEL CORPORATION
Inventor： Koba Natroshvili
IPC: G06N99/00
CPC classification number: G06N20/00
Abstract: Techniques are disclosed for improving classification performance in supervised learning. In accordance with some embodiments, a multiclass support vector machine (SVM) having three or more classes may be converted to a plurality of binary problems that then may be reduced via one or more reduced-set methods. The resultant reduced-set (RS) vectors may be combined together in one or more joint lists, along with the original support vectors (SVs) of the different binary classes. Each binary problem may be re-trained using the joint list(s) by applying a reduction factor (RF) parameter to reduce the total quantity of RS vectors. In re-training, different kernel methods can be combined, in accordance with some embodiments. Reduction may be performed until desired classification performance is achieved. The disclosed techniques can be used, for example, to improve classification speed, accuracy, class prioritization, or a combination thereof, in the SVM training phase, in accordance with some embodiments.
Abstract translation: 公开了用于提高监督学习中的分类性能的技术。 根据一些实施例，具有三个或更多个类的多类支持向量机（SVM）可以被转换为多个二进制问题，然后可以经由一个或多个简化方法来减少。 所得到的缩减集（RS）向量可以与一个或多个联合列表一起组合在一起，连同不同二进制类的原始支持向量（SV）。 可以通过应用缩减因子（RF）参数来减少RS向量的总量，使用联合列表重新训练每个二进制问题。 在重新训练中，根据一些实施例，可以组合不同的内核方法。 可以执行减少，直到达到所需的分类性能。 根据一些实施例，所公开的技术可以用于例如在SVM训练阶段中提高分类速度，精度，类别优先级或其组合。
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公开(公告)号：US20160284087A1
公开(公告)日：2016-09-29
申请号：US14667031
申请日：2015-03-24
Applicant: INTEL CORPORATION
Inventor： Koba Natroshvili , Kay-Ulrich Scholl
CPC classification number: G06T7/80 , B60R1/00 , B60R2300/105 , B60R2300/301 , B60R2300/402 , G06T2207/10016 , G06T2207/30252
Abstract: Methods, apparatuses, and systems may provide for using the motion of a vehicle to estimate the orientation of a camera system of a vehicle relative to the vehicle. Image data may be received from a plurality of cameras positioned on the vehicle, and a first constraint set may be determined for the plurality of cameras based on a plurality of feature points in a ground plane proximate to the vehicle. A second constraint set may be determined based on one or more borders of the vehicle. One or more of the cameras may be automatically calibrated based on the first constraint set and the second constraint set.
Abstract translation: 方法，装置和系统可以提供使用车辆的运动来估计车辆相对于车辆的照相机系统的取向。 可以从位于车辆上的多个摄像机接收图像数据，并且可以基于靠近车辆的接地平面中的多个特征点为多个摄像机确定第一约束集。 可以基于车辆的一个或多个边界来确定第二约束集合。 可以基于第一约束集和第二约束集自动地校准一个或多个照相机。
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