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    STOCHASTIC TRAJECTORY PREDICTION USING SOCIAL GRAPH NETWORKS
    2.
    发明申请
    STOCHASTIC TRAJECTORY PREDICTION USING SOCIAL GRAPH NETWORKS 有权

    公开(公告)号：US20220292867A1

    公开(公告)日：2022-09-15

    申请号：US17635792

    申请日：2019-09-16

    Applicant: INTEL CORPORATION

    Inventor： Lidan ZHANG ,  Qi She ,  Ping Guo

    IPC: G06V40/10 ,  G06N3/04 ,  G06T7/73 ,  G06T7/12 ,  G06T7/246 ,  G06V10/82

    Abstract: Systems, methods, apparatuses, and computer program products to provide stochastic trajectory prediction using social graph networks. An operation may comprise determining a first feature vector describing destination features of a first person depicted in an image, generating a directed graph for the image based on all people depicted in the image, determining, for the first person, a second feature vector based on the directed graph and the destination features, sampling a value of a latent variable from a learned prior distribution, the latent variable to correspond to a first time interval, and generating, based on the sampled value and the feature vectors by a hierarchical long short-term memory (LSTM) executing on a processor, an output vector comprising a direction of movement and a speed of the direction of movement of the first person at a second time interval, subsequent to the first time interval.

    CONTINUOUS LEARNING FOR OBJECT TRACKING
    3.
    发明申请
    CONTINUOUS LEARNING FOR OBJECT TRACKING 有权

    公开(公告)号：US20210312642A1

    公开(公告)日：2021-10-07

    申请号：US17057084

    申请日：2019-01-03

    Applicant: Intel Corporation

    Inventor： Lidan ZHANG ,  Ping GUO ,  Haibing REN ,  Yimin ZHANG

    IPC: G06T7/246 ,  G06K9/62 ,  G06N20/00

    Abstract: A long-term object tracker employs a continuous learning framework to overcome drift in the tracking position of a tracked object. The continuous learning framework consists of a continuous learning module that accumulates samples of the tracked object to improve the accuracy of object tracking over extended periods of time. The continuous learning module can include a sample pre-processor to refine a location of a candidate object found during object tracking, and a cropper to crop a portion of a frame containing a tracked object as a sample and to insert the sample into a continuous learning database to support future tracking.

    OBJECT IDENTIFICATION BASED ON ADAPTIVE LEARNING
    4.
    发明公开
    OBJECT IDENTIFICATION BASED ON ADAPTIVE LEARNING 审中-公开

    公开(公告)号：US20230206612A1

    公开(公告)日：2023-06-29

    申请号：US17999709

    申请日：2020-06-24

    Applicant: Intel Corporation

    Inventor： Sangeeta Ghangam MANEPALLI ,  Siew Wen CHIN ,  Ping GUO ,  Qi SHE ,  Yingzhe SHEN ,  Lidan ZHANG ,  Yimin ZHANG

    IPC: G06V10/80 ,  G06V40/16 ,  G06V10/774 ,  G06V10/776 ,  G06V10/62

    CPC classification number: G06V10/809 ,  G06V10/62 ,  G06V10/774 ,  G06V10/776 ,  G06V40/161

    Abstract: Disclosed herein are systems, methods, and devices for using adaptive learning to identify objects. An object-identifying device performs a first object identification based on one or more features of a first modality of an object retrieved from an image frame including the object and a first database including first modality identification features. A second object identification is performed based on one or more features of a second modality of the object retrieved from the image frame and a second database including second modality identification features. The second database is updated by adaptively learning a new second modality identification feature according to a first identification result of the first object identification. The second object identification is trained with the updated second database and determines a final identification result by integrating a first identification result of the first object identification and a second identification result of the second object identification.

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