Video tagging / metadata extraction
Video tagging, also called metadata extraction, is the process of adding descriptive labels to a video, such as topics, people, objects, locations, brands and moods, either automatically or by hand. AI models generate tags by analyzing frames, audio and on-screen text, often with timestamps. The tags become searchable metadata that helps organize, recommend and monetize a video library.
Why it matters
Tags are how a library gets organized, searched, recommended and sold to advertisers. Manual tagging does not scale and varies from person to person. Automated tagging makes every asset findable on day one and gives recommendation and ad systems the signals they need.
How it works
- AI models analyze frames, audio and on-screen text across the video.
- Detected objects, people, places, topics and brands are turned into labels.
- Labels are scored for confidence and checked against a controlled list of allowed tags.
- Tags are saved as metadata, often with timestamps, for search and recommendations.
What is video metadata extraction?
Video metadata extraction is pulling descriptive information out of a video automatically, such as topics, people, objects, spoken keywords and on-screen text. Unlike technical metadata like resolution or duration, this is content metadata about what the video is about. It is stored alongside the file so systems can search, filter and recommend it.
How do I tag videos automatically?
Run your videos through a video AI service that returns tags from visuals, speech and text. Map the returned labels to your own list of categories so tags stay consistent, and set a confidence threshold to drop weak guesses. Spot-check a sample regularly, because a few wrong tags can send viewers or ads to the wrong content.
Where it's used
News publishers tag footage by person, place and topic so editors can find it later. Creator platforms and OTT services use tags to improve recommendations, search and ad matching across large libraries.
Where it usually goes wrong, and how FastPix handles it
Auto-generated tags often pile up as loose keywords that nobody maps to a real taxonomy, so search and recommendations barely improve.
Through the FastPix API, in-video AI extracts named entities from transcripts and generates summaries and chapters. Teams can store these outputs as descriptive metadata for search and organization. New uploads arrive with searchable names and topics, without editors logging them by hand.
