Methods, systems, and media for detecting two-dimensional videos placed on a sphere in abusive spherical video content

    公开(公告)号:US11810353B2

    公开(公告)日:2023-11-07

    申请号:US17163841

    申请日:2021-02-01

    Applicant: Google LLC

    Inventor: Filip Pavetic

    Abstract: Methods, systems, and media for analyzing spherical video content are provided. More particularly, methods, systems, and media for detecting two-dimensional videos placed on a sphere in abusive spherical video content are provided. In some embodiments, the method comprises: receiving an identifier of a spherical video content item, wherein the spherical video content item has a plurality of views; selecting a first frame of the spherical video content item; projecting the first frame of the spherical video content item to a two-dimensional region using a projection defined by a mapping according to which neighboring points of the first frame are mapped to respective neighboring points of the region, and one or more contiguous portions of the frame are each mapped to a corresponding plurality of contiguous portions of the region; identifying an area within the region which meets a criterion indicative of the region having a likelihood above a threshold of including a particular type of content; in response to identifying the area within the region which meets the criterion, analyzing the identified area of the region using a video fingerprinting technique; and, in response to determining that content associated with the identified area of the region matches a reference content item of a plurality of reference content items using the video fingerprinting technique, generating an indication of the match in association with the identifier of the spherical video content item.

    Randomly generated blobs to improve object-detection training for framed video content

    公开(公告)号:US11734908B2

    公开(公告)日:2023-08-22

    申请号:US17228750

    申请日:2021-04-13

    Applicant: GOOGLE LLC

    CPC classification number: G06V10/25 G06N3/08 G06V10/26 G06V10/462

    Abstract: Generating a training image for use in training a region-of-interest detector that is trained to detect regions-of-interest within images includes generating a closed geometric shape; filling the closed geometric shape with a filler to obtain a blob; overlaying the blob on an edge of an image to obtain the training image, where the image includes a region-of-interest and a background region, and where the edge separates the region-of-interest from the background region; and using the training image to train the region-of-interest detector to detect a boundary of the region-of-interest. An input to the region-of-interest detector in a training phase includes the training image and a first indication of coordinates of the region-of-interest in the training image. An output of the region-of-interest detector includes a second indication of an area of the training image and a probability of the area of the training image being the region-of-interest.

    METHODS, SYSTEMS, AND MEDIA FOR DETECTING AND TRANSFORMING ROTATED VIDEO CONTENT ITEMS

    公开(公告)号:US20200162770A1

    公开(公告)日:2020-05-21

    申请号:US16615366

    申请日:2017-12-13

    Applicant: Google LLC

    Abstract: Methods, systems, and media for detecting and transforming rotated video content items are provided. The method comprises: receiving a video having a plurality of frames, wherein the video is associated with a first fingerprint; determining a rotation value associated with at least a portion of the plurality of frames to obtain a plurality of rotation values; determining an overall rotation value associated with the video based on a portion of the plurality of rotation values; determining whether at least one additional fingerprint of the video should be generated based on the overall rotation value; in response to determining that the at least one additional fingerprint of the video should be generated based on the overall rotation value, selecting a rotation transform based on the overall rotation value that rotates the plurality of frames of the video to an initial rotation position; applying the rotation transform to at least a portion of the plurality of frames of the video; generating a second fingerprint that represents the transformed video; and comparing the second fingerprint of the transformed video to a plurality of fingerprints associated with reference videos to determine whether the video corresponding to the transformed video matches one of the reference videos.

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