Gopro, Inc.

Jonathan Wills

Desmond Chik

Daniel Tse

Brian G Schunck

Mahlet Getachew

Rajvikram Singh

Systems and method of generating video summaries are presented herein. Information defining a video may be obtained. The video may include a set of frame images. Parameter values for parameters of individual frame images of the video may be determined. Interest weights for the frame images may be determined. An interest curve for the video that characterizes the video by interest weights as a function of progress through the set of frame images may be generated. One or more curve attributes of the interest curve may be identified and one or more interest curve values of the interest curve that correspond to individual curve attributes may be determined. Interest curve values of the interest curve may be compared to threshold curve values. A subset of frame images of the video to include within a video summary of the video may be identified based on the comparison.

Systems and methods for generating a video summary

A system and/or method configured to determine highlight segments. Content files that define content in a content segment set may be obtained. A highlight segment set may be determined from the content segment set. Determining the highlight segment set may include iterating (a)-(c) for multiple iterations. At (a), individual content segments included in the content segment set may be selected as a selected content segment for inclusion in the highlight segment set. At, (b) diversity scores for content segments that are (i) included in the content segment set and (ii) not yet selected for inclusion in the highlight segment set may be determined. At (c), one or more of the content segments may be disqualified for inclusion in the highlight segment set for future iterations based on the diversity scores.

Systems and methods for determining highlight segment sets

Video information defining video content may be accessed. The video content may have a progress length and include a capture of an activity and a sub-activity at one or more moments in the progress length. Portions of the video content may be associated individually with values of an interest metric such that the values of the interest metric as a function of progress through the video content form an interest curve for the video content. The activity and the sub-activity captured within the video content may be identified. An activity metric modifier for the interest curve at the one or more moments in the progress length may be determined based on the identification of the activity and the identification of the sub-activity. The interest curve may be modified at the one or more moments based on the activity metric modifier.

Systems and methods for identifying interesting moments within videos

A first command may be received from a client computing platform. The first command may include proxy visual content. The proxy visual content may represent visual content stored on the client computing platform. An identifier may be associated with the proxy visual content. The identifier may be transmitted to the client computing platform. The identifier may be associated with the visual content stored on the client computing platform. Edits to the visual content based upon the proxy visual content may be determined at a remote computing platform. Instructions may be transmitted from the remote computing platform to the client computing platform. The instructions may include the identifier. The instructions may be configured to cause the client computing platform to process the edits on the visual content.

Systems and methods that effectuate transmission of workflow between computing platforms

A system and/or method configured to determine a sample frame order for analyzing a video. The video may have multiple frames ordered in a sequence from a beginning to an end. A first sample frame order for analyzing the video may be determined. Determining the first sample frame order may include determining an initial frame for a first iteration, and determining secondary frames for a second iteration. Determining the initial frame and the secondary frames may be based on a function of frame position in the sequence of frames. The initial frame may be associated with a first sample position, and the secondary frames may be associated with secondary sample positions in the sample frame order. A first feature of the video may be determined based on an analysis of the frames in the video performed on the frames in the first sample frame order.

Systems and methods for determining a sample frame order for analyzing a video

System and/or method configured to determine a sample frame order for analyzing a video. The video may have multiple frames ordered in a sequence from a beginning to an end. Segments of the video may be determined based on an analysis of the frames of the video. A first sample frame order for analyzing the video may be determined based on multiple iterations performed on individual ones of the segments of the video. Determining the first sample frame order may include determining initial frames for a first iteration, and determining secondary frames for a second iteration based on a function of frame position in the sequence of frames. The initial frames and the secondary frames may be associated with sample positions in the sample frame order. A first feature of the video may be determined based on an analysis of the frames performed in the first sample frame order.

Systems and methods for determining a sample frame order for analyzing a video via segments

A system and/or method configured to determine highlight segment sets. Content files that define content in content segment sets may be obtained. Individual highlight segment sets of content segments may be determined from the content segment sets. Determining a first highlight segment set of content segments included in the first content segment may include iterating (a)-(c) for multiple iterations. At (a), individual content segments included in the first content segment set may be selected as a selected content segment for inclusion in the first highlight segment set. At, (b) diversity scores for content segments that are (i) included in the first content segment set and (ii) not yet selected for inclusion in the first highlight segment set may be determined. At (c), one or more of the content segments may be disqualified for inclusion in the first highlight segment set for future iterations based on the diversity scores.

Images may be extracted from a video. The images may be grouped into image groups. Numbers and types of classified visuals within the images may be detected. Individual types of classified visuals may correspond to individual classification weights. Image classification weights for the images may be determined based on the numbers and the types of classified visuals and the individual classification weights. Interest weights for the images may be determined based on the image classification weights and the sizes of the image groups to which the individual images belong. An interest curve may be generated based on the interest weights. A retime curve may be generated based on the interest curve. Time lapse images to be included in the time lapse video may be determined based on the images and the retime curve. The time lapse video may be generated based on the time lapse images.

Systems and methods for generating a time lapse video

A first command may be received from a client computing platform. The first command may include a proxy image. The proxy image may represent an image stored on the client computing platform. An identifier may be associated with the proxy image. The identifier may be transmitted to the client computing platform. The identifier may be associated with the image stored on the client computing platform. Edits to the image based upon the proxy image may be determined at a remote computing platform. Instructions may be transmitted from the remote computing platform to the client computing platform. The instructions may include the identifier. The instructions may be configured to cause the client computing platform to process the edits on the image.

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San Mateo, CA, United States of America

Jonathan Wills