Google Inc.

Sasi Inguva

Anil Christopher Kokaram

Damien Kelly

Andrew Crawford

Hugh Denman

Stuart Bennett

Joan Lasenby

Thierry Foucu

Yao-Chung Lin

Sam John

Neil Aylon Charles Birkbeck

Husain Bengali

A media items to be shared with users of a content sharing service are identified. Each of the media items corresponds to a video recording generated by a client device that depicts one or more objects corresponding to a real-world event and/or a geographic location. A location of the client device that generated the video recording corresponding to a respective media item of the media items is determined based on image features depicted in a set of frames of the video recording. A request for content associated with at least one of the real-world event and/or the geographic location is received from another client device connected to the content sharing service. The media items and, for each of the media items, an indication of the location of the client device that generated the corresponding video recording are provided in accordance with the request for content.

Determining multiple camera positions from multiple videos

A set of media items to be shared with users of a content sharing service is identified. Each of the set of media items corresponds to a video recording generated by a client device that depicts one or more objects corresponding to a real-world event at a geographic location. A positioning of the client device that generated the video recording corresponding to a respective media item of the set of media items is determined. The positioning is determined based on image features depicted in a set of frames of the video recording. A request for content associated with at least one of the real-world event or the geographic location is received from another client device connected to the content sharing service. The set of media items and, for each of the set of media items, an indication of the determined positioning of the client device that generated the corresponding video recording is provided in accordance with the request for content.

The technology disclosed herein includes a method for determining the position of multiple cameras relative to each other. In one example, the method may include: receiving, by a processor, a first video recording of a first camera and a second video recording of a second camera; selecting a set of frames of the first video recording; determining a blurriness measure for multiple frames of the set; identifying feature points in multiple frames of the set; selecting a frame from the set of frames based on the blurriness measure and the identified feature points; and determining a position of a first camera relative to a second camera by comparing the selected frame with a frame of the second video recording.

Systems and methods for processing a video sequence are disclosed. In accordance with some implementations, The method includes determining a first set of shot-change locations associated with a cut transition in a video sequence based on variance data defined in an encoding log associated with at least one previously encoded video frame. The method further includes determining a second set of shot-change locations associated with a fading transition in the video sequence based on the variance data defined in the encoding log associated with the at least one previously encoded video frame. The method also includes processing the video sequence based on at least a portion of the first set of shot-change locations and the second set of shot-change locations.

Video content analysis and/or processing using encoding logs

A method for determining the position of multiple cameras relative to each other includes at a processor, receiving video data from at least one video recording taken by each camera; selecting a subset of frames of each video recording, including determining relative blurriness of each frame of each video recording, selecting frames having a lowest relative blurriness, counting features points in each of the lowest relative blurriness frames, and selecting for further analysis, lowest relative blurriness frames having a highest count of feature points; and processing each selected subset of frames from each video recording to estimate the location and orientation of each camera.

A method includes identifying a frame of a spherical video as a key frame, storing a compensation component based on position data of a camera in association with the key frame, and compensating for a movement of the camera in a subsequent frame of the spherical video based on the key frame and the compensation component associated with the key frame.

Spherical video stabilization based on accelerometer data

A system to facilitate analyzing and/or processing video content using encoding logs is provided. The system includes a first analysis component, a second analysis component and a transcoding component. The first analysis component determines a first set of shot-change locations associated with a cut transition in a video sequence based on an encoding log associated with at least one previously encoded video frame. The second analysis component determines a second set of shot-change locations associated with a fading transition in the video sequence based on the encoding log associated with the at least one previously encoded video frame. The transcoding component processes the video sequence based on at least a portion of the first set of shot-change locations and the second set of shot-change locations.

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Mountain View, CA, United States of America

Sasi Inguva