OTT & Media · AI in production
Highlights cut while the match is still running
A model that watches the live feed, finds the moments worth replaying, and cuts the compilation before the second half.
- Client
- A major MAM and media logistics company
- Segment
- OTT & Media, sports
- Content
- Live streams and video on demand
- Timeline
- A working real-time app after 2 months
- YouTube upload
- Vimeo upload
- Dailymotion output
Before
What was true before
Highlights were assembled by hand after the match. Each compilation waited on a person to watch the footage, find the moments and cut them, which put the output hours behind the event and capped how much soccer the business could monetise.
After
What we did
A model watches the live stream, recognises more than 15 kinds of on-pitch activity, identifies players by face and by kit, and assembles the compilation as the match runs. Replay detection picks the moments worth a second look.
Build log
How it was built
- 01
Built an ML engine that decomposes a match and compiles highlights automatically
- 02
Detected fouls, red cards, scoreboards, audience reactions and other replay-worthy moments
- 03
Identified players by face and by kit for player-specific scene search
- 04
Added replay detection that skips irrelevant scenes and ads
- 05
Processed live streams with low delay, and video on demand
- 06
Added manual filters for activities and players, and direct upload to video platforms
- 07
Found ad insertion spots and made the system trainable for new highlight types
Measured
Results
As reported in the published case.
- reduction in video processing time
- 72%
- accuracy identifying scoring moments and fouls
- 90%
- faster highlight compilation
- 3.2x
- increase in footage processing speed
- 70%
- kinds of on-pitch activity recognised
- 15+
- of delay on a live stream, at its lowest
- 10 seconds
Engagements
The services behind it
Engagement
Where this work sits
Next step
Yours would be the next one.
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