Video content is now more popular than ever.

A lot of different businesses found all kinds of use for it: training, marketing, education, entertainment, and so on.

And as it seems, the people appreciate the format: digital video viewership continues to grow over the years. Since 2019, it has risen from 2,78 billion to 3,37 in 2022.

And it’s not just TikToks and YouTube reaction videos: OTT viewership also continues to rise, expected to surpass 2 billion viewers in 2024.

But videos are not pieces of paper with words on them. So managing them is a bit more tricky. You can easily search through words in a document and create summaries, but videos are much harder for those kinds of things.

So, let’s see how we can make the software analyze the videos as easily as plain text — using cognitive cloud computing.

The problem with video content analysis

Keeping things organized is essential when you work with content on a large scale. Media companies update and rearrange their content library on a regular basis and also create new clips on top of that content.

And just like with any other content, you’re not just supposed to have it somewhere. You are supposed to have a system in place that allows you to effectively work with it.

Say you run a movie studio. You can have hundreds, if not thousands, of movies. But they are not just sitting there — you might decide to push some of them to different streaming platforms, or have TV channels rerun some of the classics.

And that means that you need to be aware of all the content you have, so that it would be easy to locate.

That also means that you might want to promote the upcoming release, so you will need to make trailers and other promotional clips for social media and TV.

Or maybe you’d like to make a compilation video recapping some of the best movies the studio has produced.

But if we are talking about the large volume of content, going in and manually digging out the movies, watching all of them, and cutting the best scenes just seems not very effective.

That’s where automation comes into play. Modern technology offers a way to understand the data within the video content so that it would be easy for software to manage said content.

You may think that it’s AI we are talking about.

Well, not really.

AI for video analysis

To be honest, Artificial Intelligence is sometimes given more credit than it deserves. Even though we call it “intelligence”, that technology can’t think in a way that humans do.

The biggest problem that AI struggles with is the data it uses. Before you even begin applying it to your workflows, you have to train the AI.

And that takes quite a bit of time to do. On top of that, if you need to use the AI for another workflow — the AI would require re-training.

Then, there are incomplete datasets. Say you have AI analyzing the movie so that it could find the most important shots and generate a trailer using those shots.

The caveat here is that, despite all the credit AI gets, it can’t perceive emotion and analyze things in the way humans do. In this case, how do you help it decide which shots of the movie are trailer-worthy?

You use workarounds. For example, you can have AI analyzing the sound of the movie and pick up on the intense music. Object recognition works as well: the system can look for such things as explosions if it’s dealing with an action movie.

But the problem is that the trailer also has to set up the theme of the movie in some way. So, the system has to understand the plot and the context of the analyzed film.

Unfortunately, that’s what AI can’t do.

Cognitive cloud computing solves video analysis problems

Cognitive cloud computing technology tackles the issue right on — it changes the way software analyzes video content.

Instead of just focusing on clues to do its job, cognitive cloud computing imitates the way human brains work — that means it can actually understand the context of the video it analyzes.

That makes it more effective at delivering consistent results. There’s no need to train the model in order to have it work with content — it can easily work with incomplete sets of information.

Another great thing about cognitive computing is the ability to make decisions based on the analyzed information. Let’s say you have a piece of content that you’re preparing to air on a certain TV channel that has strict rules about nudity. The cognitive computing system can not only find the inappropriate content but also blur or cut them out completely automatically.

You just feed it the footage and get the results in a couple of minutes.

Aside from the speed and accuracy, video analysis software powered by cognitive cloud computing can boast great versatility as well. You can use the system to do a ton of things with video, such as:

• Highlight or trailer generation. Have the software pick up the best moments of a movie, TV show, or a sporting event and compile them into a single clip;
• Nudity filtering. Automatically filter out inappropriate content to safeguard your viewers;
• Summarization. Get a short clip that includes all the important pieces of news programs;
• Credits-detection. Enable your viewers to safely skip the opening and closing credits without missing any important scenes;
• Ad-insertion. Have contextually coherent ads inserted into the footage based on the video’s context;
• Celebrity recognition. Have the system identify celebrities appearing in footage automatically;
• Content library management. You can use the data that cognitive computing software extracts out of the footage to categorize, manage, and find the content in your backlog more easily.

Bottom line

With video content becoming more popular as a tool facilitating education, training, entertainment, and other aspects of our lives, we have to figure out a more effective way to work with it.

A stack of moving frames with audio over it is not as easy to analyze as plain text.

In our collective efforts to find the best way to automate video content analysis, many engineers look into Artificial Intelligence to facilitate the thing. But AI requires a lot of prep and still fails to deliver consistent results.

So, the straight imitation of the human way of thinking works much better here. Cognitive cloud computing technology relies on deep learning, cognitive science, computer vision, probabilistic AI, and other tech to make sure that the software works as effectively and consistently as possible.

Author’s bio: “Pavel Saskovec, Technical Writer at AIHunters – a cognitive computing company providing tech solutions for the media and entertainment industry. Has extensive experience covering tech-related topics, the majority of which center around technology helping optimize the workflow of the media industry.”

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