Systems, methods, and storage media for updating a media stream manifest corresponding a media stream package are disclosed. Exemplary implementations may: receive an original manifest corresponding to a media stream and a revised manifest corresponding to an updated version of the media stream; determine an identifier of the track of the media stream based on a combination of metadata in the original manifest; determine an identifier of a corresponding track of the updated version of the media stream based on a combination of metadata in the revised manifest; determine new metadata in the revised manifest that is not included in the existing manifest; generate a delta manifest of the new metadata; and insert the new metadata into the corresponding portions of original manifest and storing the resulting data structure to a data store associated with stored media objects for the updated to thereby generate a final manifest.

The serving of content with (e.g., on) video documents may be improved in a number of ways. For example, a system may accept information defining at least one ad spot associated with at least one instance of an video document, accept offers to have ads served in the ad spot(s), and arbitrate among competing ads, using at least the offers, to determine at least one ad to be served in that ad spot(s). As another example, a system may accept relevance information for an ad, determine at least one video document using the accepted relevance information, present information about the video document(s) to an ad provider associated with the ad, and accept, from the ad provider, an offer to have its ad served with at least one of the video document(s) accepted. As yet another example, a system may accept relevance information for an video document, determine a plurality of ads relevant to the video document using the relevance information and serving constraints of the ads, and select at least one of the determined relevant ads to be served with the video document. Examples of video documents include video files published on the Internet, television programs, live or recorded talk shows, video-voice mail, segments of a video conversation, etc.

A method for processing video data is performed by a data processing unit (DPU). The method includes identifying, by the DPU of an edge device, a contextual stitching trigger event; in response to identifying the contextual stitching trigger event: identifying video data chunks, contextual metadata, indexing metadata, and authentication and authorization (AA) metadata associated with the contextual stitching trigger event; generating a virtual blob based on the video data chunks, contextual metadata, indexing metadata, and authentication and authorization AA metadata; making a first determination that a playback request is obtained from a user, wherein the playback request comprises user authentication information; and in response to the first determination: making a second determination that the user authentication information matches the AA metadata; and in response to the second determination; encoding the video data chunks into a single stream and initiating playback for the user.

Examples of the present disclosure relate to techniques providing on-demand services through the use of portable computing devices. More particularly, embodiments are related to methods and systems for allowing point-of-view streaming via mobile devices by matching users browsing a webpage with an associated real world address with a streamer with a mobile device.

An example includes a segment collector to: access impression records indicative of media access segments, the media access segments including start times and end times corresponding to media accessed by a panelist; and determine ones of the impression records that include a watermark corresponding to a first media platform presenting the media; a segment classifier to convert a first one of the impression records including the watermark to a converted impression record; and combine the converted impression record corresponding to the first media platform and a second impression record corresponding to a second media platform; and a media creditor to generate audience measurement metrics based on the combined impression records.

This application relates to a method for displaying item information performed by a computer device. The method includes: displaying a live stream image comprising a live stream host performing in a live stream environment and at least one item located in the live stream environment, and the at least one item comprising a target item; in response to an item recognition operation, displaying an item tag corresponding to the at least one item; and in response to receiving a first selection operation on an item tag, displaying an item link region. Because the item tag includes an item keyword, preliminary information corresponding to the item can be directly provided to a viewer of the live stream. When the viewer account initiates a selection operation on the item tag, an item link region including item information is displayed. In this way, the viewer can further interact with the item information.

The present disclosure belongs to the field of computer technology and relates to a data processing method and device, a computer-readable storage medium, and an electronic device. The method includes: determining an image frame of video data, and acquiring position information of a function area located in the image frame; acquiring coding information of the function area, and establishing a first mapping relationship between the coding information and the position information; and acquiring a time stamp of the image frame, and establishing a second mapping relationship between the time stamp and the first mapping relationship.

Systems, methods, and non-transitory computer-readable media are disclosed for automatic tagging of videos. In particular, in one or more embodiments, the disclosed systems generate a set of tagged feature vectors (e.g., tagged feature vectors based on action-rich digital videos) to utilize to generate tags for an input digital video. For instance, the disclosed systems can extract a set of frames for the input digital video and generate feature vectors from the set of frames. In some embodiments, the disclosed systems generate aggregated feature vectors from the feature vectors. Furthermore, the disclosed systems can utilize the feature vectors (or aggregated feature vectors) to identify similar tagged feature vectors from the set of tagged feature vectors. Additionally, the disclosed systems can generate a set of tags for the input digital videos by aggregating one or more tags corresponding to identified similar tagged feature vectors.

An example method comprises receiving, at a first digital device, video data, scanning video content of the video data for visual transitions within the video content between consecutive frames of the video data, each transition indicating significant visual transitions relative to other frames of the video data, timestamping each visual transition and create a first set of temporal video fingerprints, identifying items of metadata to be associated with the video data, identifying a location within the video data using the temporal video fingerprints for the identified items of metadata, generating a metadata index identifying each item of metadata and a location for each item of metadata relative to the video data using at least one of the temporal video fingerprints, and transmitting, at the first digital device, the video data, the first set of temporal video fingerprints, and the metadata index to a different digital device.

This disclosure describes techniques for improving user experience when servicing on-demand requests by a consumer for a hyper-specific media content in a network environment. The hyper-specific media content may include specified audio, video, etc. that relates to hyper-specific criteria (e.g., specific subject, location, and time condition). In an embodiment, the hyper-specific criteria in a consumer request are compared with stored hyper-specific criteria in a database. A prediction model is then trained on feature parameters of matching stored hyper-specific criteria to generate a trending level classification that can be associated with the hyper-specific criteria in the consumer request. The trending level classification indicates level of interest of third parties and may be used as a basis for content management.