OBJECT IDENTIFICATION BASED ON ADAPTIVE LEARNING

    公开(公告)号:US20230206612A1

    公开(公告)日:2023-06-29

    申请号:US17999709

    申请日:2020-06-24

    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.

    METHODS AND APPARATUS TO MATCH IMAGES USING SEMANTIC FEATURES

    公开(公告)号:US20210174134A1

    公开(公告)日:2021-06-10

    申请号:US16768559

    申请日:2018-03-01

    Abstract: Methods and apparatus to match images using semantic features are disclosed. An example apparatus includes a semantic labeler to determine a semantic label for each of a first set of points of a first image and each of a second set of points of a second image; a binary robust independent element features (BRIEF) determiner to determine semantic BRIEF descriptors for a first subset of the first set of points and a second subset of the second set of points based on the semantic labels; and a point matcher to match first points of the first subset of points to second points of the second subset of points based on the semantic BRIEF descriptors.

    CONTINUOUS LEARNING FOR OBJECT TRACKING

    公开(公告)号:US20210312642A1

    公开(公告)日:2021-10-07

    申请号:US17057084

    申请日:2019-01-03

    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.

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