公开(公告)号：US20210158050A1

公开(公告)日：2021-05-27

申请号：US17163841

申请日：2021-02-01

Applicant: Google LLC

Inventor： Filip Pavetic

IPC: G06K9/00 ,  G06F21/16 ,  G06T3/00

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.

公开(公告)号：US10904586B2

公开(公告)日：2021-01-26

申请号：US16615366

申请日：2017-12-13

Applicant: Google LLC

Inventor： Filip Pavetic ,  Hanna Pasula

IPC: H04N21/234 ,  G06F16/783 ,  G06F21/10

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.

公开(公告)号：US12217142B2

公开(公告)日：2025-02-04

申请号：US18520532

申请日：2023-11-27

Applicant: Google LLC

Inventor： Filip Pavetic ,  King Hong Thomas Leung ,  Dmitrii Tochilkin

IPC: G06N20/00 ,  G06F18/214 ,  G06F18/241 ,  G06V10/25 ,  G06V10/764 ,  G06V20/40

Abstract: A system and methods are disclosed for using a trained machine learning model to identify constituent images within composite images. A method may include providing data identifying a first image as input to a machine learning model trained using training data identifying a plurality of composite images that each include one or more constituent images, and determining, using one or more outputs of the trained machine learning model, that the first image is a composite image that includes a first constituent image, wherein at least a portion of the first constituent image is in a spatial area of the first image, and wherein the first constituent image corresponds to a frame of a video embedded into the first image.

公开(公告)号：US12002257B2

公开(公告)日：2024-06-04

申请号：US17536509

申请日：2021-11-29

Applicant: GOOGLE LLC

Inventor： Mayank Kandpal ,  Bakhodir Ashirmatov ,  Filip Pavetic

IPC: G06V10/774 ,  G06N20/00 ,  G06V10/74 ,  G06V10/776 ,  G06V20/40

CPC classification number: G06V10/7747 ,  G06N20/00 ,  G06V10/761 ,  G06V10/776 ,  G06V20/46

Abstract: Video content screening using a trained video screening model trained using self-supervised training includes automatically generating a training dataset by obtaining predicate screening data indicating a predicate temporal segment within a training video and a corresponding reference temporal segment within the reference video, obtaining candidate screening data for an extended temporal segment from the training video, wherein the extended temporal segment includes the predicate temporal segment and at least one frame from the training video adjacent to the predicate temporal segment, wherein the candidate screening data indicates a similarity between a screening frame from the reference video and a spatial portion of a candidate frame from the extended temporal segment, and, in response to a determination that a determined similarity between the candidate subframe including, in the automatically generated training dataset, training example data indicating the similarity between the candidate subframe and the screening frame.

公开(公告)号：US20240104435A1

公开(公告)日：2024-03-28

申请号：US18520532

申请日：2023-11-27

Applicant: Google LLC

Inventor： Filip Pavetic ,  King Hong Thomas Leung ,  Dmitrii Tochilkin

IPC: G06N20/00 ,  G06F18/214 ,  G06F18/241 ,  G06V10/25 ,  G06V10/764 ,  G06V20/40

CPC classification number: G06N20/00 ,  G06F18/214 ,  G06F18/241 ,  G06V10/25 ,  G06V10/764 ,  G06V20/40 ,  G06V20/41

Abstract: A system and methods are disclosed for using a trained machine learning model to identify constituent images within composite images. A method may include providing data identifying a first image as input to a machine learning model trained using training data identifying a plurality of composite images that each include one or more constituent images, and determining, using one or more outputs of the trained machine learning model, that the first image is a composite image that includes a first constituent image, wherein at least a portion of the first constituent image is in a spatial area of the first image, and wherein the first constituent image corresponds to a frame of a video embedded into the first image.

公开(公告)号：US10909381B2

公开(公告)日：2021-02-02

申请号：US16418399

申请日：2019-05-21

Applicant: Google LLC

Inventor： Filip Pavetic

IPC: G06K9/00 ,  G06F21/16 ,  G06T3/00

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.

公开(公告)号：US20200210709A1

公开(公告)日：2020-07-02

申请号：US16813686

申请日：2020-03-09

Applicant: Google LLC

Inventor： Filip Pavetic ,  King Hong Thomas Leung ,  Dmitrii Tochilkin

IPC: G06K9/00 ,  G06K9/62 ,  G06K9/32 ,  G06N20/00

Abstract: A system and methods are disclosed for using a trained machine learning model to identify constituent images within composite images. A method may include providing pixel data of a first image as input to the trained machine learning model, obtaining one or more outputs from the trained machine learning model, and extracting, from the one or more outputs, a level of confidence that (i) the first image is a composite image that includes a constituent image, and (ii) at least a portion of the constituent image is in a particular spatial area of the first image.

公开(公告)号：US11829854B2

公开(公告)日：2023-11-28

申请号：US17403804

申请日：2021-08-16

Applicant: Google LLC

Inventor： Filip Pavetic ,  King Hong Thomas Leung ,  Dmitrii Tochilkin

IPC: G06K9/00 ,  G06K9/62 ,  G06N20/00 ,  G06V20/40 ,  G06V10/25 ,  G06F18/214 ,  G06F18/241 ,  G06V10/764

CPC classification number: G06N20/00 ,  G06F18/214 ,  G06F18/241 ,  G06V10/25 ,  G06V10/764 ,  G06V20/40 ,  G06V20/41

Abstract: A system and methods are disclosed for using a trained machine learning model to identify constituent images within composite images. A method may include providing pixel data of a first image as input to the trained machine learning model, obtaining one or more outputs from the trained machine learning model, and extracting, from the one or more outputs, an indication that the first image is a composite image that includes a constituent image, wherein at least a portion of the constituent image is in a spatial area of the first image.

公开(公告)号：US20230169759A1

公开(公告)日：2023-06-01

申请号：US17536509

申请日：2021-11-29

Applicant: GOOGLE LLC

Inventor： Mayank Kandpal ,  Bakhodir Ashirmatov ,  Filip Pavetic

IPC: G06V10/774 ,  G06V10/776 ,  G06N20/00 ,  G06V10/74 ,  G06V20/40

CPC classification number: G06V10/7747 ,  G06V10/776 ,  G06N20/00 ,  G06V10/761 ,  G06V20/46

Abstract: Video content screening using a trained video screening model trained using self-supervised training includes automatically generating a training dataset by obtaining predicate screening data indicating a predicate temporal segment within a training video and a corresponding reference temporal segment within the reference video, obtaining candidate screening data for an extended temporal segment from the training video, wherein the extended temporal segment includes the predicate temporal segment and at least one frame from the training video adjacent to the predicate temporal segment, wherein the candidate screening data indicates a similarity between a screening frame from the reference video and a spatial portion of a candidate frame from the extended temporal segment, and, in response to a determination that a determined similarity between the candidate subframe including, in the automatically generated training dataset, training example data indicating the similarity between the candidate subframe and the screening frame.

公开(公告)号：US20220327322A1

公开(公告)日：2022-10-13

申请号：US17228750

申请日：2021-04-13

Applicant: GOOGLE LLC

Inventor： Mayank Kandpal ,  Bakhodir Ashirmatov ,  Filip Pavetic

IPC: G06K9/32 ,  G06K9/34 ,  G06K9/46 ,  G06N3/08

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.