Deep learning and machine learning for video

Aug 8, 2022

Today, video data is virtually everywhere – from YouTube videos to quality control and surveillance. Intelligent algorithms help us create, analyze, and process video data just like it happens with any other type of digital information. In this article, we will show you how two AI-related technologies – deep learning and machine learning are used when it comes to video data.

Firstly, we need to explain what we mean by video data. To explain that, let’s start with something simpler – a single image. It’s a piece of data that comprises specific dimensions (height and width) and three-color layers called RGB. Put all that together, and you end up with a single image file. Now imagine that you have thousands of such files, and they are temporally ordered – the next appears right after the previous one. Such files are referred to as frames. A collection of such frames constitutes video data. Every YouTube video, every surveillance footage, and every commercial works the exact same way. Moreover, video data is directly linked to another AI technology called computer vision. So every autonomous vehicle also generates tons of video data, continually flowing as it drives around.


The use of video data in relation to AI

Now, how can we use AI-related technologies to manage video files? There are a few options:

  • Cognitive systems: They are used with autonomous devices and vehicles. These systems enable robots and vehicles to detect objects in front of them and react accordingly (e.g., to bypass an obstacle). 
  • Metadata recognition: It can be used to ensure content compliance, e.g., to verify that this particular part of the footage was made on the given day, at the given hour.
  • Intelligent solutions: Here, we can mention smart quality assurance at factories that need no human supervision to verify the quality of the products that are being manufactured.
  • Image analysis: Frequently used in the healthcare sector, where intelligent algorithms can analyze footage made during examination (e.g., ultrasound) or surgical procedure.

With this introduction done, let’s have a look at these two technologies – deep learning and machine learning. 


What is machine learning?

Machine learning is one of the essential AI technologies that is all about machines (algorithms) learning themselves. We start with an initial (so-called training) dataset and a machine learning algorithm that’s supposed to do something based on that dataset – analyze, modify, look for specific elements, etc. We train our algorithm using data from that dataset. Based on that training, the algorithm is capable of doing the same task on new datasets. 

Suppose you want to train a machine learning algorithm that will be able to detect product defects on the production line. As a start, you need a dataset comprising faulty products (the more, the better). Such a dataset should contain high-quality images indicating specific defects. The algorithm learns based on that and, thanks to that training, can detect defective products on its own – e.g., by detecting deviations from the norm.

How to use machine learning for video

As you already know, it all starts with a dataset comprising relevant elements that we want to train our algorithm with. But that’s not sufficient. Here, we hit another bump in the road called data annotation. Your data needs to be labeled in a way that teaches the machine learning algorithm how to execute a specific task. This means tagging or labeling these datasets with the characteristics you wish your machine learning algorithm to identify. In other words, if you’re working on a defect detection system, you need a dataset that shows faulty products and indicates specific faults. This way, the algorithm will know that you want it to look for similar defects and inform about them.


What is deep learning?

Deep learning is a more advanced form of machine learning. There is one critical difference between them, though. Machine learning algorithms almost always require structured/labeled datasets (for training purposes). On the other hand, deep learning doesn’t need such extensive training. This technology uses artificial neural networks (that imitate the way the human brain works) to make decisions and analyze input data.

How to use deep learning for video

Undoubtedly, deep learning has revolutionized both AI and computer vision and made these technologies scalable. Deep learning facilitates the data extraction process by using huge sets of training data and multiple training cycles to train algorithms to do a specific job. As opposed to the manual extraction of features (in machine learning), deep learning algorithms automate the entire process and automatically extract necessary parts and elements.

Thanks to deep learning, computer vision algorithms can increase the level and complexity of their learning and deliver satisfactory performance and efficiency. Deep learning is used in processing medical videos, manufacturing quality control, military operations, autonomous vehicles, smart security surveillance, and many other applications.