Gopro, Inc.

Vincent Garcia

Tom Médioni

Maxime Schwab

Francois Lagunas

Jean Caillé

Balthazar Neveu

Francescu Santoni

Gabriel Lema

Matthieu Rouif

Systems and method of identifying a horizon depicted in an image are presented herein. Information defining an image may be obtained. The image may include visual content comprising an array of pixels. The array may include pixel rows. Parameter values for a set of pixel parameters of individual pixels of the image may be determined. Individual average parameter values of the individual pixel parameters of the pixels in the individual pixel rows may be determined. Based on the average parameter values a pixel row may be identified as depicting a horizon in the image.

Systems and methods for horizon identification in an image

Visual content may be captured by an image capture device. The characteristics of the visual content may be determined based on assessment of separate visual content and without assessment of the visual content. The assessment of the separate visual content may include identification of thing(s) depicted within the separate visual content. The separate visual content may be temporally and/or spatially separate from the visual content.

Systems and methods for characterizing visual content

A video, such as a spherical video, may include motion due to motion of one or more image capture devices during capture of the video. Motion of the image capture devices during the capture of the video may cause the playback of the video to appear jerky/shaky. The video may be stabilized by using both a horizontal feature and a fixed feature captured within the video.

Systems and methods for stabilizing videos

Video information defining video content may be obtained. The video content may include video frames and may have a progress length. The video frames may be encoded into video packets, with the video packets being of particular sizes. One or more size criteria for detecting a given moment within the video content may be obtained. The sizes of the video packets may be compared with the one or more size criteria. One or more sets of the video packets that satisfy the one or more size criteria may be identified. One or more portions of the video content having video frames defined by the set(s) of video packets that satisfy the one or more size criteria may be identified as the given moment within the video content. Storage of the identification of the given moment within the video content in a storage medium may be effectuated.

Systems and methods for detecting moments within videos

Media items may be obtained. The media items may be characterized by capture information indicating a capture time and a capture location of individual media items. The media items may be clustered into scenes based on proximity of the capture times of the media items. The scenes may be clustered into collections based on proximity of the capture times and/or the capture locations of the media items within the scenes. The collections may be iteratively clustered into higher collections based on proximity of the capture times and/or the capture locations of the media items within the collections. One or more collections may be identified for inclusion in a media compilation based on a size of the identified collection(s). A media compilation, including one or more of the media items included in the identified collection(s), may be generated.

Systems and methods for creating compilations based on hierarchical clustering

Systems and methods of distinguishing between feature depicted in an image are presented herein. Information defining an image may be obtained. The image may include visual content comprising an array of pixels. The array may include pixel rows. An identification of a pixel row in an image may be obtained. Distances of individual pixels and/or groups of pixels from the identified row of pixels may be determined. Parameter values for a set of pixel parameters of individual pixels of the image may be determined. Based on one or more of the distances from the identified row of pixels, parameter values of one or more pixel parameters, and/or other information, individual pixels and/or groups of pixels may be classified as one of a plurality of image features.

Systems and methods to distinguish between features depicted in images

Audio content may have a duration. The audio content may be segmented into audio segments. Individual audio segments may correspond to a portion of the duration. Mel frequency spectral power features, Mel frequency cepstral coefficient features, and energy features of the audio segments may be determined. Feature vectors of the audio segments may be determined based on the Mel frequency spectral power features, the Mel frequency cepstral coefficient features, and the energy features. The feature vectors may be processed through a support vector machine. The support vector machine may output predictions on whether the audio segments contain speech. One or more of the audio segments may be identified as containing speech based on filtering the predictions and comparing the filtered predictions to a threshold. Storage of the identification of the one or more of the audio segments as containing speech in one or more storage media may be effectuated.

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Issy-les-Moulineaux, France

Vincent Garcia