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61.发明授权公开(公告)号：US10832084B2
公开(公告)日：2020-11-10
申请号：US16526306
申请日：2019-07-30
Applicant: NEC Laboratories America, Inc.
Inventor： Quoc-Huy Tran , Mohammed E. Fathy Salem , Muhammad Zeeshan Zia , Paul Vernaza , Manmohan Chandraker
Abstract: A method for estimating dense 3D geometric correspondences between two input point clouds by employing a 3D convolutional neural network (CNN) architecture is presented. The method includes, during a training phase, transforming the two input point clouds into truncated distance function voxel grid representations, feeding the truncated distance function voxel grid representations into individual feature extraction layers with tied weights, extracting low-level features from a first feature extraction layer, extracting high-level features from a second feature extraction layer, normalizing the extracted low-level features and high-level features, and applying deep supervision of multiple contrastive losses and multiple hard negative mining modules at the first and second feature extraction layers. The method further includes, during a testing phase, employing the high-level features capturing high-level semantic information to obtain coarse matching locations, and refining the coarse matching locations with the low-level features to capture low-level geometric information for estimating precise matching locations.
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公开(公告)号：US10762359B2
公开(公告)日：2020-09-01
申请号：US16029167
申请日：2018-07-06
Applicant: NEC Laboratories America, Inc.
Inventor： Quoc-Huy Tran , Mohammed E. F. Salem , Muhammad Zeeshan Zia , Paul Vernaza , Manmohan Chandraker
Abstract: Systems and methods for detecting traffic scenarios include an image capturing device which captures two or more images of an area of a traffic environment with each image having a different view of vehicles and a road in the traffic environment. A hierarchical feature extractor concurrently extracts features at multiple neural network layers from each of the images, with the features including geometric features and semantic features, and for estimating correspondences between semantic features for each of the images and refining the estimated correspondences with correspondences between the geometric features of each of the images to generate refined correspondence estimates. A traffic localization module uses the refined correspondence estimates to determine locations of vehicles in the environment in three dimensions to automatically determine a traffic scenario according to the locations of vehicles. A notification device generates a notification of the traffic scenario.
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63.发明授权公开(公告)号：US10679075B2
公开(公告)日：2020-06-09
申请号：US16029126
申请日：2018-07-06
Applicant: NEC Laboratories America, Inc.
Inventor： Quoc-Huy Tran , Mohammed E. F. Salem , Muhammad Zeeshan Zia , Paul Vernaza , Manmohan Chandraker
Abstract: Systems and methods for correspondence estimation and flexible ground modeling include communicating two-dimensional (2D) images of an environment to a correspondence estimation module, including a first image and a second image captured by an image capturing device. First features, including geometric features and semantic features, are hierarchically extract from the first image with a first convolutional neural network (CNN) according to activation map weights, and second features, including geometric features and semantic features, are hierarchically extracted from the second image with a second CNN according to the activation map weights. Correspondences between the first features and the second features are estimated, including hierarchical fusing of geometric correspondences and semantic correspondences. A 3-dimensional (3D) model of a terrain is estimated using the estimated correspondences belonging to the terrain surface. Relative locations of elements and objects in the environment are determined according to the 3D model of the terrain. A user is notified of the relative locations.
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公开(公告)号：US10678257B2
公开(公告)日：2020-06-09
申请号：US16146202
申请日：2018-09-28
Applicant: NEC Laboratories America, Inc.
Inventor： Samuel Schulter , Paul Vernaza , Manmohan Chandraker , Menghua Zhai
IPC: G05D1/02 , G05D1/00 , G06T17/05 , G06T7/194 , G06T7/514 , G06T7/50 , G06K9/00 , G06T15/20 , G06T7/11 , G06T3/00 , G06T15/40
Abstract: Systems and methods for generating an occlusion-aware bird's eye view map of a road scene include identifying foreground objects and background objects in an input image to extract foreground features and background features corresponding to the foreground objects and the background objects, respectively. The foreground objects are masked from the input image with a mask. Occluded objects and depths of the occluded objects are inferred by predicting semantic features and depths in masked areas of the masked image according to contextual information related to the background features visible in the masked image. The foreground objects and the background objects are mapped to a three-dimensional space according to locations of each of the foreground objects, the background objects and occluded objects using the inferred depths. A bird's eye view is generated from the three-dimensional space and displayed with a display device.
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公开(公告)号：US20200050900A1
公开(公告)日：2020-02-13
申请号：US16526073
申请日：2019-07-30
Applicant: NEC Laboratories America, Inc.
Inventor： Samuel Schulter , Ziyan Wang , Buyu Liu , Manmohan Chandraker
Abstract: A method for implementing parametric models for scene representation to improve autonomous task performance includes generating an initial map of a scene based on at least one image corresponding to a perspective view of the scene, the initial map including a non-parametric top-view representation of the scene, implementing a parametric model to obtain a scene element representation based on the initial map, the scene element representation providing a description of one or more scene elements of the scene and corresponding to an estimated semantic layout of the scene, identifying one or more predicted locations of the one or more scene elements by performing three-dimensional localization based on the at least one image, and obtaining an overlay for performing an autonomous task by placing the one or more scene elements with the one or more respective predicted locations onto the scene element representation.
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Patent Agency Ranking
公开(公告)号：US10474882B2
公开(公告)日：2019-11-12
申请号：US15888747
申请日：2018-02-05
Applicant: NEC Laboratories America, Inc.
Inventor： Xiang Yu , Kihyuk Sohn , Manmohan Chandraker
Abstract: A video surveillance system is provided. The system includes a device configured to capture an input image of a subject located in an area. The system further includes a processor. The processor estimates, using a three-dimensional Morphable Model (3DMM) conditioned Generative Adversarial Network, 3DMM coefficients for the subject of the input image. The subject varies from an ideal front pose. The processor produces, using an image generator, a synthetic frontal face image of the subject of the input image based on the input image and coefficients. An area spanning the frontal face of the subject is made larger in the synthetic than in the input image. The processor provides, using a discriminator, a decision of whether the subject of the synthetic image is an actual person. The processor provides, using a face recognition engine, an identity of the subject in the input image based on the synthetic and input images.
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67.发明授权公开(公告)号：US10402690B2
公开(公告)日：2019-09-03
申请号：US15801688
申请日：2017-11-02
Applicant: NEC Laboratories America, Inc.
Inventor： Paul Vernaza , Manmohan Chandraker
Abstract: Systems and methods for training semantic segmentation. Embodiments of the present invention include predicting semantic labeling of each pixel in each of at least one training image using a semantic segmentation model. Further included is predicting semantic boundaries at boundary pixels of objects in the at least one training image using a semantic boundary model concurrently with predicting the semantic labeling. Also included is propagating sparse labels to every pixel in the at least one training image using the predicted semantic boundaries. Additionally, the embodiments include optimizing a loss function according the predicted semantic labeling and the propagated sparse labels to concurrently train the semantic segmentation model and the semantic boundary model to accurately and efficiently generate a learned semantic segmentation model from sparsely annotated training images.
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公开(公告)号：US10331974B2
公开(公告)日：2019-06-25
申请号：US15709897
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: G06K9/00 , G06K9/62 , G06F17/50 , G06N3/02 , G06T11/60 , G06T15/40 , G05D1/02 , G08G1/16 , G06T7/73 , G06N3/08 , G06T15/10 , B60W30/00 , G08G1/0962 , G06T7/55 , G06K9/46
Abstract: An action recognition system and method are provided. The action recognition system includes an image capture device configured to capture an actual image depicting an object. The action recognition system includes a processor configured to render, based on a set of 3D CAD models, synthetic images with corresponding intermediate shape concept labels. The processor is configured to form a multi-layer CNN which jointly models multiple intermediate shape concepts, based on the rendered synthetic images. The processor is configured to perform an intra-class appearance variation-aware and occlusion-aware 3D object parsing on the actual image by applying the CNN thereto to generate an image pair including a 2D and 3D geometric structure of the object. The processor is configured to control a device to perform a response action in response to an identification of an action performed by the object, wherein the identification of the action is based on the image pair.
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公开(公告)号：US10290197B2
公开(公告)日：2019-05-14
申请号：US15637533
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
Abstract: A mass transit surveillance system and corresponding method are provided. The mass transit surveillance system includes a camera configured to capture an input image of a subject purported to be a baby and presented at a mass transit environment. The mass transit surveillance system further includes a memory storing a deep learning model configured to perform a baby detection task for the mass transit environment. The mass transit surveillance 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.
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公开(公告)号：US20190138814A1
公开(公告)日：2019-05-09
申请号：US16115630
申请日：2018-08-29
Applicant: NEC Laboratories America, Inc.
Inventor： Samuel Schulter , Wongun Choi , Tuan Hung Vu , Manmohan Chandraker
CPC classification number: G06T7/73 , G06K9/00744 , G06K9/00979 , G06K9/6232 , G06K9/629 , G06T7/20 , G06T7/269 , G06T2207/10016 , G06T2207/20016 , G06T2207/20076 , G06T2207/20081 , G06T2207/20084
Abstract: A computer-implemented method, system, and computer program product are provided for object detection utilizing an online flow guided memory network. The method includes receiving a plurality of videos, each of the plurality of videos including a plurality of frames. The method also includes generating, with a feature extraction network, a frame feature map for a current frame of the plurality of frames. The method additionally includes aggregating a memory feature map from the frame feature map and previous memory feature maps from previous frames on a plurality of time axes, with the plurality of time axes including a first time axis at a first frame increment and a second time axis at a second frame increment. The method further includes predicting, with a task network, an object from the memory feature map. The method also includes controlling an operation of a processor-based machine to react in accordance with the object.
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