Abstract: Various arrangements for performing an initial setup process of a sleep tracking device are presented. User input may be received that requests a sleep tracking setup process be performed. In response to the user input, a detection process may be performed based on data received from the radar sensor to determine whether a user is present and static. In response to the detection process determining that the user is present and static, a consistency analysis may be performed over a time period to assess a duration of time that the user is present and static. Based on the consistency analysis, sleep tracking may be activated such that when the user is detected in bed via the radar sensor, the user's sleep is tracked.

Abstract: 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.

Abstract: Various arrangements for performing an initial setup process of a sleep tracking device are presented. User input may be received that requests a sleep tracking setup process be performed. In response to the user input, a detection process may be performed based on data received from the radar sensor to determine whether a user is present and static. In response to the detection process determining that the user is present and static, a consistency analysis may be performed over a time period to assess a duration of time that the user is present and static. Based on the consistency analysis, sleep tracking may be activated such that when the user is detected in bed via the radar sensor, the user's sleep is tracked.

Abstract: 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.

Abstract: Methods and apparatus for the generation of interpolated frames of video data. In one embodiment, the interpolated frames of video data are generated by obtaining two or more frames of video data from a video sequence; determining frame errors for the obtained two or more frames from the video sequence, determining whether the frame errors exceed a threshold value; performing a multi-pass operation; performing a single-pass operation; performing frame blending; performing edge correction; and generating the interpolated frame of image data.

Abstract: 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.

Abstract: 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.

Abstract: A set of sensor information conveyed by sensor output signals may be accessed. The sensor output signals may be generated by a set of sensors. The set of sensor information may characterize an event monitored by the set of sensors. A multi-feature convolutional neural network may be trained using a branch-loss function. The branch-loss function may include individual loss functions for individual sensor information and one or more combined loss functions for combined sensor information. The set of sensor information may be processed through the multi-feature convolutional neural network. A classification of the event may be obtained from the multi-feature convolutional network based on the set of sensor information.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Methods and apparatus for the generation of interpolated frames of video data. In one embodiment, the interpolated frames of video data are generated by obtaining two or more frames of video data from a video sequence; determining frame errors for the obtained two or more frames from the video sequence, determining whether the frame errors exceed a threshold value; performing a multi-pass operation; performing a single-pass operation; performing frame blending; performing edge correction; and generating the interpolated frame of image data.

Abstract: 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.

Abstract: Systems and method of identifying activities and/or events represented in a video are presented herein. An activity and/or event may be represented in a video by virtue of one or both of an entity moving with a capture device during capture of the video preforming the activity and/or event, or the video portraying one or more entities performing the activity and/or event. Activity types may be characterized by one or more of common movements, equipment, spatial context, and/or other features. Events may be characterized by one or both of individual movements and/or sets of movements that may routinely occur during performance of an activity. The identification of activities and/or events represented in a video may be based on one or more spectrogram representations of sensor output signals of one or more sensors coupled to a capture device.

Abstract: Methods and apparatus for the generation of interpolated frames of video data. In one embodiment, the interpolated frames of video data are generated by obtaining two or more frames of video data from a video sequence; determining frame errors for the obtained two or more frames from the video sequence, determining whether the frame errors exceed a threshold value; performing a multi-pass operation; performing a single-pass operation; performing frame blending; performing edge correction; and generating the interpolated frame of image data.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.