Abstract: This disclosure relates to the embedding of visual objects into the image content of a video by a visual embed specialist, whilst maintaining the security of the video. A low-resolution version of the video content is sent by the video owner to the specialist for analysis to identify parts of the video that are suitable for visual object insertion. A high resolution version of those identified parts of the video is then sent to the specialist for visual object insertion. The specialist may then return the modified parts of the video and the content owner create a final version of the high-resolution video by replacing the relevant parts of the high-resolution video with the modified parts.

Abstract: The present disclosure relates to a computer implemented method, computer program and apparatus for classifying object insertion opportunities in a video by identifying at least one object insertion opportunity in a scene of the video, identifying a focus of attention in the scene, determining a proximity value for each of the at least one object insertion opportunity based at least in part on the object insertion opportunity and the focus of attention, wherein the proximity value is indicative of a distance between the respective at least one object insertion opportunity and the focus of attention in the scene, and classifying each of the at least one object insertion opportunity based at least in part on the proximity value determined for each respective at least one object insertion opportunity.

Abstract: The present disclosure relates to a video content discovery apparatus, system, method and computer program. In one aspect of the disclosure there is provided a video content discovery module configured to receive a content query, retrieve, from one or more information sources, text that relates to the content query, process the retrieved text, at least in part using Natural Language Processing, to transform the content query to a set comprising one or more video content descriptors, and identify one or more video segments of a plurality of available video segments, using the one or more video content descriptors.

Abstract: A system and method of incorporating additional video objects into source video data to produce output video data. A method includes identifying segments of the source video data, selecting identified segments for the inclusion of additional video objects, creating an intermediate working version of the source video data including video material corresponding to the selected segments, creating metadata which identifies at least one frame within the source video data which corresponds to the selected segments, transmitting the intermediate working version to a remote system for the creation of additional video data including additional video objects to be included in the output video data, receiving video file data associated with the additional video data, obtaining the additional video data based on the video file data, retrieving metadata and incorporating the additional video data with the source video data on the basis of the retrieved metadata to produce the output video data.

Abstract: This disclosure relates to the embedding of visual objects into the image content of a video by a visual embed specialist, whilst maintaining the security of the video. A low-resolution version of the video content is sent by the video owner to the specialist for analysis to identify parts of the video that are suitable for visual object insertion. A high resolution version of those identified parts of the video is then sent to the specialist for visual object insertion. The specialist may then return the modified parts of the video and the content owner create a final version of the high-resolution video by replacing the relevant parts of the high-resolution video with the modified parts.

Abstract: This disclosure relates to the embedding of visual objects into the image content of a video by a visual embed specialist, whilst maintaining the security of the video. A low-resolution version of the video content is sent by the video owner to the specialist for analysis to identify parts of the video that are suitable for visual object insertion. A high resolution version of those identified parts of the video is then sent to the specialist for visual object insertion. The specialist may then return the modified parts of the video and the content owner create a final version of the high-resolution video by replacing the relevant parts of the high-resolution video with the modified parts.

Abstract: Aspects of the present disclosure aim to improve upon methods and systems for the incorporation of additional material into source video data. In particular, the method of the present disclosure may use a pre-existing corpus of source video data to produce, test and refine a prediction model for enabling the prediction of the characteristics of placement opportunities. The model may be created using video analysis techniques which obtain metadata regarding placement opportunities and also through the identification of categorical characteristics relating to the source video which may be provided as metadata with the source video, or obtaining through image processing techniques described below. Using the model, the method and system may then be used to create a prediction of insertion zone characteristics for projects for which source video is not yet available, but for which information corresponding to the identified categorical characteristics is known.

Abstract: A method of digital advertising, comprising: receiving a plurality of video material sources; optionally converting video material from a format of said sources into one or more placement operating formats; placing a digital advertising component into video material; and outputting the video material with the digital advertising component integrated within it for distribution. Preferred embodiments of the invention involve generating a market for placement zones suitable for receiving digital advertising components, wherein a market is generated by for example a bidding process against one or more available placement zones or offering one or more available placement zones with an indication its value/price. Also disclosed are computer apparatus and systems for performing the above-mentioned methods and related methods, and a system and method for management and manipulation of digital assets.

Abstract: This disclosure relates to the embedding of visual objects into the image content of a video by a visual embed specialist, whilst maintaining the security of the video. A low-resolution version of the video content is sent by the video owner to the specialist for analysis to identify parts of the video that are suitable for visual object insertion. A high resolution version of those identified parts of the video is then sent to the specialist for visual object insertion. The specialist may then return the modified parts of the video and the content owner create a final version of the high-resolution video by replacing the relevant parts of the high-resolution video with the modified parts.

Abstract: The present disclosure relates to a computer implemented method, computer program and apparatus for classifying object insertion opportunities in a video by identifying at least one object insertion opportunity in a scene of the video, identifying a focus of attention in the scene, determining a proximity value for each of the at least one object insertion opportunity based at least in part on the object insertion opportunity and the focus of attention, wherein the proximity value is indicative of a distance between the respective at least one object insertion opportunity and the focus of attention in the scene, and classifying each of the at least one object insertion opportunity based at least in part on the proximity value determined for each respective at least one object insertion opportunity.

Abstract: Aspects of the present disclosure aim to improve upon methods and systems for the incorporation of additional material into source video data. In particular, the method of the present disclosure may use a pre-existing corpus of source video data to produce, test and refine a prediction model for enabling the prediction of the characteristics of placement opportunities. The model may be created using video analysis techniques which obtain metadata regarding placement opportunities and also through the identification of categorical characteristics relating to the source video which may be provided as metadata with the source video, or obtaining through image processing techniques described below. Using the model, the method and system may then be used to create a prediction of insertion zone characteristics for projects for which source video is not yet available, but for which information corresponding to the identified categorical characteristics is known.

Abstract: The method disclosure provides methods, systems and computer programs for identification of candidate video insertion object types using machine learning. Machine learning is used for at least part of the processing of the image contents of a plurality of frames of a scene of a source video. The processing includes identification of a candidate insertion zone for the insertion of an object into the image content of at least some of the plurality of frames and determination of an insertion zone descriptor for the identified candidate insertion zone, the insertion zone descriptor comprising a candidate object type indicative of a type of object that is suitable for insertion into the candidate insertion zone.

Abstract: Aspects of the present disclosure aim to improve upon methods and systems for the incorporation of additional material into source video data. In particular, the method of the present disclosure may use a pre-existing corpus of source video data to produce, test and refine a prediction model for enabling the prediction of the characteristics of placement opportunities. The model may be created using video analysis techniques which obtain metadata regarding placement opportunities and also through the identification of categorical characteristics relating to the source video which may be provided as metadata with the source video, or obtaining through image processing techniques described below. Using the model, the method and system may then be used to create a prediction of insertion zone characteristics for projects for which source video is not yet available, but for which information corresponding to the identified categorical characteristics is known.

Abstract: A method of digital advertising, comprising: receiving a plurality of video material sources; optionally converting video material from a format of said sources into one or more placement operating formats; placing a digital advertising component into video material; and outputting the video material with the digital advertising component integrated within it for distribution. Preferred embodiments of the invention involve generating a market for placement zones suitable for receiving digital advertising components, wherein a market is generated by for example a bidding process against one or more available placement zones or offering one or more available placement zones with an indication its value/price. Also disclosed are computer apparatus and systems for performing the above-mentioned methods and related methods, and a system and method for management and manipulation of digital assets.

Abstract: A method of digital advertising, comprising: receiving a plurality of video material sources; optionally converting video material from a format of said sources into one or more placement operating formats; placing a digital advertising component into video material; and outputting the video material with the digital advertising component integrated within it for distribution. Preferred embodiments of the invention involve generating a market for placement zones suitable for receiving digital advertising components, wherein a market is generated by for example a bidding process against one or more available placement zones or offering one or more available placement zones with an indication its value/price. Also disclosed are computer apparatus and systems for performing the above-mentioned methods and related methods, and a system and method for management and manipulation of digital assets.

Abstract: The method disclosure provides methods, systems and computer programs for identification of candidate video insertion object types using machine learning. Machine learning is used for at least part of the processing of the image contents of a plurality of frames of a scene of a source video. The processing includes identification of a candidate insertion zone for the insertion of an object into the image content of at least some of the plurality of frames and determination of an insertion zone descriptor for the identified candidate insertion zone, the insertion zone descriptor comprising a candidate object type indicative of a type of object that is suitable for insertion into the candidate insertion zone.

Abstract: A system and method of incorporating additional video objects into source video data to produce output video data. A method includes identifying segments of the source video data, selecting identified segments for the inclusion of additional video objects, creating an intermediate working version of the source video data including video material corresponding to the selected segments, creating metadata which identifies at least one frame within the source video data which corresponds to the selected segments, transmitting the intermediate working version to a remote system for the creation of additional video data including additional video objects to be included in the output video data, receiving video file data associated with the additional video data, obtaining the additional video data based on the video file data, retrieving metadata and incorporating the additional video data with the source video data on the basis of the retrieved metadata to produce the output video data.

Abstract: Aspects of the present disclosure aim to improve upon methods and systems for the incorporation of additional material into source video data. In particular, the method of the present disclosure may use a pre-existing corpus of source video data to produce, test and refine a prediction model for enabling the prediction of the characteristics of placement opportunities. The model may be created using video analysis techniques which obtain metadata regarding placement opportunities and also through the identification of categorical characteristics relating to the source video which may be provided as metadata with the source video, or obtaining through image processing techniques described below. Using the model, the method and system may then be used to create a prediction of insertion zone characteristics for projects for which source video is not yet available, but for which information corresponding to the identified categorical characteristics is known.

Abstract: A system and method of incorporating additional video objects into source video data to produce output video data. A method includes identifying segments of the source video data, selecting identified segments for the inclusion of additional video objects, creating an intermediate working version of the source video data including video material corresponding to the selected segments, creating metadata which identifies at least one frame within the source video data which corresponds to the selected segments, transmitting the intermediate working version to a remote system for the creation of additional video data including additional video objects to be included in the output video data, receiving video file data associated with the additional video data, obtaining the additional video data based on the video file data, retrieving metadata and incorporating the additional video data with the source video data on the basis of the retrieved metadata to produce the output video data.

Abstract: An apparatus and method for automatic detection of insertion zones within pre-recorded video material are provided. The apparatus includes a video analysis unit configured to automatically determine at least one candidate insertion zone within the pre-recorded video material suitable for receiving additional material to be inserted and configured to generate zone meta data defining attributes of the insertion zone, and an insertion module configured to receive additional material for insertion and arranged to generate an output representative of the pre-recorded video material with the additional material placed on an insertion zone such that the additional material adopts the image attributes of the insertion zone as the pre-recorded video material is played. The automatic determination of at least one insertion zone is based on one or more of feature shape, size, duration, movement, color.