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    Surveillance system using deep network flow for multi-object tracking
    21.
    发明授权
    Surveillance system using deep network flow for multi-object tracking 有权

    公开(公告)号：US10402983B2

    公开(公告)日：2019-09-03

    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: G06K9/00 ,  G06T7/20 ,  G06T7/77 ,  G06T7/70 ,  H04N7/18 ,  G06K9/62

    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.

    Deep network flow for multi-object tracking
    25.
    发明授权
    Deep network flow for multi-object tracking 有权

    公开(公告)号：US10332264B2

    公开(公告)日：2019-06-25

    申请号：US15695565

    申请日：2017-09-05

    Applicant: NEC Laboratories America, Inc.

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

    IPC: G06K9/00 ,  G06K9/62 ,  G06T7/20 ,  G06T7/70 ,  G06T7/77 ,  H04N7/18

    Abstract: A multi-object tracking system and method are provided. The multi-object tracking system includes at least one camera configured to capture a set of input images of a set of objects to be tracked. The multi-object tracking system further 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 multi-object tracking system also includes a processor configured to (i) detect the objects and track locations of the objects by applying the learning model to the set of input images in a multi-object tracking task, and (ii), provide a listing of the objects and the locations of the objects for the multi-object tracking task. A bi-level optimization is used to minimize a loss defined on a solution of the linear program.

    Smuggling detection system
    26.
    发明授权
    Smuggling detection system 有权

    公开(公告)号：US10290196B2

    公开(公告)日：2019-05-14

    申请号：US15637433

    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: G08B21/02 ,  G06N20/00 ,  G06F16/583 ,  G06F17/30 ,  G06K9/00 ,  G06N99/00 ,  G08B21/04 ,  G08B21/24

    Abstract: A smuggling detection system and corresponding method are provided. The smuggling detection system includes a camera configured to capture an input image of a subject purported to be a baby. The smuggling detection system further includes a memory storing a deep learning model configured to perform a baby detection task for a smuggling detection application. The smuggling 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.

    LONG-TAIL LARGE SCALE FACE RECOGNITION BY NON-LINEAR FEATURE LEVEL DOMAIN ADAPTION
    27.
    发明申请
    LONG-TAIL LARGE SCALE FACE RECOGNITION BY NON-LINEAR FEATURE LEVEL DOMAIN ADAPTION 审中-公开

    公开(公告)号：US20190095699A1

    公开(公告)日：2019-03-28

    申请号：US16145578

    申请日：2018-09-28

    Applicant: NEC Laboratories America, Inc.

    Inventor： Xiang Yu ,  Xi Yin ,  Kihyuk Sohn ,  Manmohan Chandraker

    IPC: G06K9/00 ,  G06N3/08 ,  G06K9/62 ,  G08B13/196 ,  G08B15/00 ,  H04N7/18

    Abstract: A computer-implemented method, system, and computer program product are provided for facial recognition. The method includes receiving, by a processor device, a plurality of images. The method also includes extracting, by the processor device with a feature extractor utilizing a convolutional neural network (CNN) with an enlarged intra-class variance of long-tail classes, feature vectors for each of the plurality of images. The method additionally includes generating, by the processor device with a feature generator, discriminative feature vectors for each of the feature vectors. The method further includes classifying, by the processor device utilizing a fully connected classifier, an identity from the discriminative feature vector. The method also includes control an operation of a processor-based machine to react in accordance with the identity.

    Unsupervised matching in fine-grained datasets for single-view object reconstruction
    28.
    发明授权
    Unsupervised matching in fine-grained datasets for single-view object reconstruction 有权

    公开(公告)号：US10204299B2

    公开(公告)日：2019-02-12

    申请号：US15342766

    申请日：2016-11-03

    Applicant: NEC Laboratories America, Inc.

    Inventor： Manmohan Chandraker ,  Angjoo Kim

    IPC: G06K9/00 ,  G06N3/04 ,  G06F17/30 ,  G06T7/33 ,  G06T7/50

    Abstract: A computer-implemented method for training a deep learning network is presented. The method includes receiving a first image and a second image, mining exemplar thin-plate spline (TPS) to determine transformations for generating point correspondences between the first and second images, using artificial point correspondences to train the deep neural network, learning and using the TPS transformation output through a spatial transformer, and applying heuristics for selecting an acceptable set of images to match for accurate reconstruction. The deep learning network learns to warp points in the first image to points in the second image.

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