公开(公告)号：US20180137365A1

公开(公告)日：2018-05-17

申请号：US15711373

申请日：2017-09-21

Applicant: NEC Laboratories America, Inc.

Inventor： Samuel Schulter ,  Wongun Choi ,  Bharat Singh ,  Manmohan Chandraker

IPC: G06K9/00 ,  G06T9/00 ,  G06K9/32

CPC classification number: G06T7/74 ,  B60W30/09 ,  B60W30/0956 ,  G06F3/0482 ,  G06K9/00744 ,  G06K9/00771 ,  G06K9/00785 ,  G06K9/00791 ,  G06K9/00805 ,  G06K9/3241 ,  G06N3/02 ,  G06T7/20 ,  G06T7/246 ,  G06T7/292 ,  G06T9/002 ,  G06T2200/28 ,  G06T2207/10016 ,  G06T2207/20084 ,  G06T2207/30232 ,  G06T2210/12 ,  G08B21/02

Abstract: An action recognition system and method are provided. The system includes an image capture device configured to capture a video sequence formed from image frames and depicting a set of objects. The system includes a processor configured to detect the objects to form object detections. The processor is configured to track the object detections over the frames to form tracked detections. The processor is configured to generate for a current frame, responsive to conditions, sparse object proposals for a current location of an object based on: (i) the tracked detections of the object from an immediately previous frame; and (ii) detection proposals for the object derived from the current frame. The processor is configured to control a hardware device to perform a response action in response to an identification of an action type of an action performed by the object, the identification being based on the sparse object proposals.

公开(公告)号：US20180130216A1

公开(公告)日：2018-05-10

申请号：US15695625

申请日：2017-09-05

Applicant: NEC Laboratories America, Inc.

Inventor： Samuel Schulter ,  Wongun Choi ,  Paul Vernaza ,  Manmohan Chandraker

IPC: G06T7/20 ,  G06T7/70 ,  H04N7/18

CPC classification number: G06T7/20 ,  G06K9/00771 ,  G06K9/6274 ,  G06T7/70 ,  G06T7/77 ,  G06T2207/20076 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30232 ,  G06T2207/30241 ,  H04N7/18 ,  H04N7/188

Abstract: A surveillance system and method are provided. The surveillance system includes at least one camera configured to capture a set of images of a given target area that includes a set of objects to be tracked. The surveillance system includes a memory storing a learning model configured to perform multi-object tracking by jointly learning arbitrarily parameterized and differentiable cost functions for all variables in a linear program that associates object detections with bounding boxes to form trajectories. The surveillance system includes a processor configured to perform surveillance of the target area to (i) detect the objects and track locations of the objects by applying the learning model to the images in a surveillance task that uses the multi-object tracking, and (ii), provide a listing of the objects and their locations for surveillance task. A bi-level optimization is used to minimize a loss defined on a solution of the linear program.

公开(公告)号：US20180129910A1

公开(公告)日：2018-05-10

申请号：US15709748

申请日：2017-09-20

Applicant: NEC Laboratories America, Inc.

Inventor： Muhammad Zeeshan Zia ,  Quoc-Huy Tran ,  Xiang Yu ,  Manmohan Chandraker ,  Chi Li

IPC: G06K9/62 ,  G06T15/40 ,  G06K9/00 ,  G06N3/02 ,  G06F17/50

CPC classification number: G06K9/6256 ,  B60T2201/022 ,  B60W30/00 ,  G05D1/0221 ,  G06F17/5009 ,  G06K9/00201 ,  G06K9/00208 ,  G06K9/00624 ,  G06K9/00771 ,  G06K9/00805 ,  G06K9/4628 ,  G06K9/6255 ,  G06N3/02 ,  G06N3/084 ,  G06T7/55 ,  G06T7/74 ,  G06T11/60 ,  G06T15/10 ,  G06T15/40 ,  G06T2207/20101 ,  G06T2207/30261 ,  G06T2210/22 ,  G08G1/0962 ,  G08G1/166 ,  H04N7/00

Abstract: A system and method are provided. The system includes an image capture device configured to capture an actual image depicting an object. The system also includes a processor. The processor is configured to render, based on a set of 3D Computer Aided Design (CAD) models, a set of synthetic images with corresponding intermediate shape concept labels. The processor is also configured to form a multi-layer Convolutional Neural Network (CNN) which jointly models multiple intermediate shape concepts, based on the rendered synthetic images. The processor is further configured to perform an intra-class appearance variation-aware and occlusion-aware 3D object parsing on the actual image by applying the CNN to the actual image to output an image pair including a 2D geometric structure and a 3D geometric structure of the object depicted in the actual image.

公开(公告)号：US09905104B1

公开(公告)日：2018-02-27

申请号：US15637360

申请日：2017-06-29

Applicant: NEC Laboratories America, Inc. ,  NEC Hong Kong Limited

Inventor： Manmohan Chandraker ,  Wongun Choi ,  Eric Lau ,  Elsa Wong ,  Guobin Chen

IPC: G08B23/00 ,  G08B21/02 ,  G08B21/24 ,  G08B21/04

CPC classification number: G08B21/0205 ,  G06F17/30256 ,  G06F17/30259 ,  G06K9/00067 ,  G06N99/005 ,  G08B21/0208 ,  G08B21/0222 ,  G08B21/0461 ,  G08B21/24

Abstract: A baby detection system and corresponding method are provided. The baby detection system includes a camera configured to capture an input image of a subject purported to be a baby and presented at an electronic-gate system. The baby detection system further includes a memory storing a deep learning model configured to perform a baby detection task for an electronic-gate application corresponding to the electronic-gate system. The baby detection system also includes a processor configured to apply the deep learning model to the input image to provide a baby detection result of either a presence or an absence of an actual baby in relation to the subject purported to be the baby. The baby detection task is configured to evaluate one or more different distractor modalities corresponding to one or more different physical spoofing materials to prevent baby spoofing for the baby detection task.

公开(公告)号：US20180025242A1

公开(公告)日：2018-01-25

申请号：US15637465

申请日：2017-06-29

Applicant: NEC Laboratories America, Inc. ,  NEC Hong Kong Limited

Inventor： Manmohan Chandraker ,  Xiang Yu ,  Eric Lau ,  Elsa Wong

IPC: G06K9/00 ,  G06N99/00

CPC classification number: G06F21/32 ,  G06F21/6218 ,  G06F2221/2133 ,  G06K9/00221 ,  G06K9/00228 ,  G06K9/00255 ,  G06K9/00281 ,  G06K9/00288 ,  G06K9/00624 ,  G06K9/00791 ,  G06K9/00906 ,  G06K9/4652 ,  G06K9/66 ,  G06N99/005 ,  G07C9/00158 ,  G07C9/00166 ,  H04L63/0861 ,  H04L63/1483

Abstract: A facility access control system and corresponding method are provided. The facility access control system includes a camera configured to capture an input image of a subject attempting to enter or exit a restricted facility. The facility access control system further includes a memory storing a deep learning model configured to perform multi-task learning for a pair of tasks including a liveness detection task and a face recognition task. The facility access control system also includes a processor configured to apply the deep learning model to the input image to recognize an identity of the subject in the input image regarding being authorized for access to the facility and a liveness of the subject. The liveness detection task is configured to evaluate a plurality of different distracter modalities corresponding to different physical spoofing materials to prevent face spoofing for the face recognition task.

公开(公告)号：US09489768B2

公开(公告)日：2016-11-08

申请号：US14073726

申请日：2013-11-06

Applicant: NEC Laboratories America, Inc.

Inventor： Yingze Bao ,  Manmohan Chandraker ,  Yuanqing Lin ,  Silvio Savarese

IPC: G06T17/00 ,  G06T3/00 ,  G06T7/00

CPC classification number: G06T17/00 ,  G06T3/00 ,  G06T7/579 ,  G06T2207/20076 ,  G06T2207/20081

Abstract: A method to reconstruct 3D model of an object includes receiving with a processor a set of training data including images of the object from various viewpoints; learning a prior comprised of a mean shape describing a commonality of shapes across a category and a set of weighted anchor points encoding similarities between instances in appearance and spatial consistency; matching anchor points across instances to enable learning a mean shape for the category; and modeling the shape of an object instance as a warped version of a category mean, along with instance-specific details.

Abstract translation: 一种重建对象的3D模型的方法包括：利用处理器从各种视点接收包括对象的图像的一组训练数据; 学习一个先前的包括一个描述一个类别的形状的共同性的平均形状，以及编码外观和空间一致性之间的实例之间的相似性的一组加权锚点; 在实例之间匹配锚点，以便学习类别的平均形状; 并将对象实例的形状建模为类别的翘曲版本，以及实例特定的细节。

公开(公告)号：US09336601B2

公开(公告)日：2016-05-10

申请号：US14528704

申请日：2014-10-30

Applicant: NEC Laboratories America, Inc.

Inventor： Manmohan Chandraker

IPC: G06K9/00 ,  G06T7/00

CPC classification number: G06T7/0055 ,  G06T7/514 ,  G06T7/55 ,  G06T2207/10021

Abstract: A computer vision method that includes deriving a relationship of spatial and temporal image derivatives of an object to bidirectional reflectance distribution function (BRDF) derivatives under camera motion, and deriving with a processor a quasilinear partial differential equation for solving surfaced depth for orthographic projections using the relationship of spatial and temporal image derivatives without requiring knowledge of the BRDF. The method may further recover surface depth for an object with unknown BRDF under perspective projection.

Abstract translation: 一种计算机视觉方法，其包括在摄像机运动下导出对象的空间和时间图像导数与双向反射分布函数（BRDF）导数的关系，以及使用处理器推导出准线性偏微分方程，以使用 空间和时间图像衍生物的关系，而不需要BRDF的知识。 该方法可以进一步恢复具有未知BRDF的物体在透视投影下的表面深度。

公开(公告)号：US09148650B2

公开(公告)日：2015-09-29

申请号：US13858040

申请日：2013-04-06

Applicant: NEC Laboratories America, Inc.

Inventor： Manmohan Chandraker ,  Shiyu Song

IPC: H04N13/02 ,  G06K9/00 ,  G06T7/00

CPC classification number: H04N13/204 ,  G06T7/579 ,  G06T7/74 ,  G06T2207/30244 ,  G06T2207/30252

Abstract: Systems and methods are disclosed for multithreaded visual odometry by acquired with a single camera on-board a vehicle; using 2D-3D correspondences for continuous pose estimation; and combining the pose estimation with 2D-2D epipolar search to replenish 3D points.

Abstract translation: 公开了通过用车上单个摄像机采集的用于多线程视觉测距的系统和方法; 使用2D-3D对应连续姿态估计; 并将姿态估计与2D-2D对极搜索相结合，以补充3D点。

公开(公告)号：US09070202B2

公开(公告)日：2015-06-30

申请号：US14184766

申请日：2014-02-20

Applicant: NEC Laboratories America, Inc.

Inventor： Manmohan Chandraker ,  Shiyu Song ,  Yuanqing Lin ,  Xiaoyu Wang

IPC: G06K9/00 ,  G06T7/20 ,  G06T7/00

CPC classification number: G06T7/2033 ,  G06K9/00805 ,  G06T7/246 ,  G06T7/277 ,  G06T7/579 ,  G06T7/73 ,  G06T2207/10016 ,  G06T2207/20076 ,  G06T2207/20081 ,  G06T2207/30252

Abstract: Systems and methods are disclosed for autonomous driving with only a single camera by moving object localization in 3D with a real-time framework that harnesses object detection and monocular structure from motion (SFM) through the ground plane estimation; tracking feature points on moving cars a real-time framework to and use the feature points for 3D orientation estimation; and correcting scale drift with ground plane estimation that combines cues from sparse features and dense stereo visual data.

Abstract translation: 公开的系统和方法仅用单个摄像机进行自主驾驶，通过利用来自运动（SFM）的对象检测和单目结构通过接地平面估计的实时框架来移动3D物体定位; 跟踪移动汽车上的特征点实时框架并使用特征点进行3D定位估计; 并且通过地面平面估计来校正尺度漂移，其结合来自稀疏特征和密集立体视觉数据的线索。

公开(公告)号：US20150117758A1

公开(公告)日：2015-04-30

申请号：US14528704

申请日：2014-10-30

Applicant: NEC Laboratories America, Inc.

Inventor： Manmohan Chandraker

IPC: G06T7/00

CPC classification number: G06T7/0055 ,  G06T7/514 ,  G06T7/55 ,  G06T2207/10021

Abstract: A computer vision method that includes deriving a relationship of spatial and temporal image derivatives of an object to bidirectional reflectance distribution function (BRDF) derivatives under camera motion, and deriving with a processor a quasilinear partial differential equation for solving surfaced depth for orthographic projections using the relationship of spatial and temporal image derivatives without requiring knowledge of the BRDF. The method may further recover surface depth for an object with unknown BRDF under perspective projection.

Abstract translation: 一种计算机视觉方法，其包括在摄像机运动下导出对象的空间和时间图像导数与双向反射分布函数（BRDF）导数的关系，以及使用处理器推导出准线性偏微分方程，以使用 空间和时间图像衍生物的关系，而不需要BRDF的知识。 该方法可以进一步恢复具有未知BRDF的物体在透视投影下的表面深度。