Improve the content searchability of your archives by generating time-coded metadata across your assets.
Perform searches at breakneck speeds at a granular level and enable quick content creation and improved monetization opportunities.
Reduce manually-reliant video tagging tasks and metadata correction and free up valuable workforce bandwidth for creative tasks.
Drive better ROI for existing content with smarter and faster searches for repackaging existing video content.
Enable deep searches within your media content archives and unlock new use cases with these tailor-made workflows
Generate granular time-coded metadata for all the visual aspects within your video content such as player faces, emotions, football pitch locales. Metadata can be generated for locales like benches, and the audience stands with varied camera shot-angles. A beauty shot or an aerial view can capture specific entities like yellow cards, red cards, or league trophies.
Unlock the hidden value within your media archive by generating second-level metadata for the visual aspects present in content such as character faces, emotions, age, gender, generic locales, camera shot-angles, frame color palette, generic entities, acoustic events like music, silence and more
Generate time-coded metadata for the visual aspects present in news content such as prominent personalities, anchors, and field reporters. Identify prominent dignitaries, production aspects, telecast formats, split-screens, logistics, and information such as story, sub-story, genres, and format.
Automated detection of characters within a video, based on unique facial features
Automated detection of age and gender of characters within a video
Automated detection of scene locations within digital video
Automated detection of RGB color scheme and luminance within a video
Automated detection of individual players within a video based on facial features
Automated detection of sports-specific camera angles like beauty shots, and aerial views within a match
Automated detection of anchors, reporters, celebrities, and guests within news video based on facial features.
Automated detection of camera angles (close-up, medium, wide-angle) for each shot within a video to understand the shot composition of the video
Automated detection of objects within digital video
Automated detection of acoustic events like speech, silence, and noise within a video to understand the audio profile of the video
Automated detection of players' emotions, spectators and others within a video to understand their sentiments at each moment in a game
Automated detection of pitch locations like benches, and audience stands within match videos
Automated detection of sports-specific entities such as yellow and red cards, and league trophies within a match
Automated detection of the club, and sponsor logos within a match.
Detection of single and split-screen views within the digital video
Detection of overlaid graphics within the digital video.
Automated detection of in-studio/on-location settings within the digital news video
Detection of story tracks and genre within a digital news video.
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