There are a growing number of solutions that utilise machine learning and computer vision techniques (debatably assuming the “AI” moniker) to automatically extract this type of data from assets. Generally, they should be able to ingest files and streams, perform some form of analysis to detect items of interest (e.g. faces, objects, logos, speech, quality markers, etc.) and give a value to the items detected (e.g. recognise an object as an oak tree), and then present the data in some way for further consumption.
The challenge for users is to find a solution which is applicable to their business, rather than using a service which is too generic that can’t easily be tuned to specific business requirements. For example, we recently heard of a well-known public cloud provider’s analysis algorithms identifying a “clock” (the countdown timer which typically appears before an item of broadcast content) as an automotive speedometer. Which is not altogether surprising, but poses a challenge if the requirement is to find all the clocks in an archive and perform optical character recognition (OCR) tasks.
Similarly, the requirement may be to find images of a particular employee, but they’re not a celebrity and the chances of a public analysis service being able to recognise them is very low. Furthermore, they are a private individual who doesn’t want their identity to be stored on the internet.
There are, of course, similar challenges associated with the location of the AI services in relation to the content to be analysed. If the archive is in the cloud but the incoming live stream is delivered down an SDI cable, then where should the solution be deployed?
Perhaps unsurprisingly for such a young market, there is some jostling amongst vendors and solutions providers to find the right approach and develop the best software to address the myriad media content management requirements of an enterprise against a landscape of data protection and privacy. As a business, this is the time to help the vendors shape the future of this AI as one thing is for sure; the amount of media content is only going to increase and, consequently, so are the problems associated with managing it and maximising its potential.
This guest post was written by Alex Buchanan, former Chief Operating Officer of NMR and Project Manager of the ReCAP project. NMR build and implement tools to help companies manage and deliver media content. ReCAP (Realtime Content Analysis and Processing) is a project co-funded by a grant from the European Union’s Horizon 2020, led by NMR.