Most comments may have ignored this so far, but this is an interesting AI based research.

At the end of the article is this excerpt

>> Results from this research were generated using an AI computer vision model developed by the research team on Bristol’s Isambard-AI, the UK’s most powerful AI supercomputer. Isambard-AI powered the analysis of 172.6 hours of live match footage from all 104 games played between 11 June and 19 July 2026.

So, basically now merchandizers, auditors, compliance monitors, etc. have a tool using which they can quantify whether broadcaster complied with contractual requirements of showing their ads/merch/logos as per contract. And if they missed, then they can quantify the delta - to sue the broadcaster for the balance as well.

Also, it can enable many countries, where certain content is not allowed, then this tool can simply give them data points without having to employ someone, or have someone review the output of the AI tooling.

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thevinter 2 hours ago | parent | next [-]

I'm sure this was already easily doable for years by using plain and boring computer vision

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saintmaxi 11 minutes ago | parent | next [-]

I worked for a company in 2020 that did ad analysis with computer vision - specifically in sports, quantifying value for sponsors based on exposure on team socials (accounting for logo size, positioning, etc in photos + videos) - I was a humble annotator https://www.blinkfire.com

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GuB-42 an hour ago | parent | prev | next [-]

It was easily doable for even more years using plain and boring MK1 eyeballs and a stopwatch.

If you are spending millions on ads, you can pay a couple of guys to watch TV and take notes.

	▲	mapt 13 minutes ago \| parent [-]
		For the World Cup, this was always easy. It was not easy for your local triple A baseball game being streamed to 500 viewers. This approach scales down.

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gps372 an hour ago | parent | prev | next [-]

Plain and boring computer vision would have still required a team who would identify frames to sample before processing, and then annotate, generalize and classify the data.

Not an expert on this but I don't think same kind of manual effort is required anymore.

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OccamsMirror an hour ago | parent | prev [-]

You probably don't even need vision. The audio on adverts is noticeably different.

	▲	vasco an hour ago \| parent [-]
		There's no audio on the adverts in question. > The analysis counted advertising built into the match footage (e.g. on the hoardings), rather than commercials shown during advertising breaks

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brookst an hour ago | parent | prev | next [-]

This is also being done in retail, where manufacturers and/or distributors pay for preferred placement (end caps, eye level, etc), but how do you check that e.g. Safeway is really doing that across a couple of thousand stores?

You hire minimum wage people to walk around stores with cameras. This was actually done with notepads occasionally, but so much less expensive tondo with AI.

	▲	gps372 10 minutes ago \| parent [-]
		True, in-store planogram compliance and floor-plan compliance have got in focus again. It never lost the focus to be honest, just that now there is an expectation to do it while ensuring nothing fall through the cracks. But there is a much more stringent privacy requirement here since customers walking in those stores haven't really signed up for this analysis. Hence, difficult to just setup the camera and take continuous data for analysis.

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amelius an hour ago | parent | prev [-]

There's a lot of data extraction being done for every game. It already uses tons of AI, and has been using it for years. Doing it by hand would have been more surprising.

What's also surprising is that despite all this data we can't automate those soccer player jobs yet.