How Deep Learning Helps With Real-Time Video Processing

In this blog article, we will focus on real-time video processing with deep learning and examine where it is used.
Rabia Gül
5 minutes

How Deep Learning Helps With Real-Time Video Processing

As artificial intelligence is becoming more and more present in our lives, we are becoming more familiar with concepts such as machine learning and deep learning. 

In particular, deep learning is frequently encountered in computer vision and related solutions, another AI function. It is one of the most widely used techniques for analyzing and processing images and videos.

Analyzing real-time video is a complex process. We often use deep learning algorithms to accomplish this. In this article, we will focus on this topic.

The topics you will find in this article are as follows: 

  •  What Is The Deep Learning Model of AI
  • Deep Learning Video Processing
  • Real-Time Video Processing With Deep Learning: Benefits and Applications
  •  Cameralyze Real-Time Solutions
  • Bottom Line 

Enjoy your reading!

real-time video processing

What Is The Deep Learning Model of AI

It would be a good starting point, to begin with, what deep learning is.

Deep learning is a sub-branch of machine learning and ultimately artificial intelligence. It is a set of algorithms, methods, and systems that enable machines, or artificial intelligence, to perform tasks that typically require human intelligence.

Deep learning has many differences from machine learning. For this reason, it is possible to say that they are more advanced than machine learning algorithms. In machine learning, the way algorithms learn and work is linear and usually requires structured data. 

Deep learning algorithms, on the other hand, work hierarchically. This is where "layers" come into play. These structures mimic the way the human brain works. Information can be processed and transferred from one layer to another. These algorithms are called "deep" to emphasize the layers.

Deep learning can connect and learn from data that is unstructured and contains different sets. Usually, there is no need for human supervision for learning. The algorithm does the same work repeatedly and improves with each repetition.

Deep learning algorithms generate a lot of data and can process it without the need for human assistance. A deep learning algorithm can not only learn what an object is, but it can also learn and distinguish between objects that are "not it". 

Deep Learning Video Processing

Nowadays, videos, like other types of data, are very important assets and are of great importance for data processing. These videos can be a social media live stream or a security camera recording. Real-time video processing is crucial for fast and reliable data tracking.

Processing real-time video requires the decoding, processing, and re-encoding of each frame.

With real-time processing of videos, it makes things quite complex. It is therefore important that the data to be processed is extremely clean. Deep learning models make real-time video processing safer and can be used in many different areas.

The fact that deep-learning models are easier and faster to learn and do not require humans to learn is one of the most important reasons for using them in real-time video processing.  There are two ways to enable real-time video processing. Both of these methods are preferable for processing. One is file splitting, which allows algorithms to run in parallel. The other method is to create a pipeline architecture.

So where is deep-learning-based real-time video processing used and what are the examples? The answer is in the rest of our article.

Real-Time Video Processing With Deep Learning: Benefits and Applications

Real-time video processing based on deep learning can be used in many different fields. Especially in security, this technology is of great importance. So where and how exactly is it used? Here is the answer: 

1. Self-Driving Cars 

 It would be fair to say that deep learning models are the enabling technology for self-driving vehicles. Deep learning models enable the movement of vehicles by simultaneously analyzing the images received from the cameras around the vehicles. Object detection and human detection functions play an important role here.

Images are processed by algorithms in real time. Objects on the road - other vehicles, people, traffic signs, traffic lights, or stones - are analyzed. Deep learning algorithms enable vehicles to act accordingly.

The expected development for the moment is to teach models of different scenarios. In other words, it is aimed to analyze sudden and unexpected events in real-time by the vehicle system.

2.  Work Safety 

Real-time video processing has also become very important in the field of occupational safety. Here again, object detection and human detection solutions are important. For example, security camera images are analyzed in real time by deep learning algorithms.

Here, the location could be a factory. Thanks to object detection, algorithms can detect in real-time workers who are not using their protective equipment. In this way, it is possible to prevent work accidents before any problems occur. For the algorithms, this is a very simple process.

3. Schools and Kindergartens

Deep learning algorithms can track motion simultaneously in real-time. It is also possible to use these systems in combination with facial recognition. Let's give a few examples of how this can be used. Imagine real-time processing of security camera footage in and around the garden of a kindergarten. Object tracking and facial recognition are also used here.

With object tracking, children who go out of the garden boundaries or try to go out of the garden boundaries can be tracked and necessary precautions can be taken before any problems are encountered. With facial recognition, images can be matched with databases in real-time to identify criminal offenders or potential criminals who are prohibited from being around schools. 

3. E-Sports 

Real-time deep learning video analytics can be used to analyze player movements. In this way, improvements and modeling can be made and possible cheating can be prevented.

The use of deep learning algorithms and solutions in real-time video analysis provides significant opportunities in many areas ranging from autonomous vehicles to occupational safety or security. They can alert security teams or prevent accidents. They can detect potential negative events before they happen and prevent negative consequences. 

Cameralyze Real-Time Solutions 

Thanks to Cameralyze Live, you can use your live streaming monitoring system online without the need to build pipelines or deal with algorithms, without the need to install any software. Use Cameralyze Live for your live broadcasts or for monitoring your security cameras!

With Cameralyze Live, you can monitor your security cameras, provide your occupational safety solutions, control your live broadcasts or use Cameralyze Live for dashcams. It makes no difference!

With Cameralyze Edge, you can aggregate your data without any security concerns.

Thanks to AI-powered Cameralyze solutions, you can get the solution you are looking for in minutes.

 Bottom Line

Real-time video processing is essential for handling your data in video format. This allows you to improve in crucial areas such as security and safety and provides the solutions you need.

Deep learning algorithms and models provide you with all the solutions you need for real-time video processing. However, these algorithms are quite complex to prepare and use. That's why it's so important to work with ready-made platforms.

With Cameralyze, you can access all AI-powered solutions without the need for codes and algorithms. You can develop your business with the web-based platform without any technical background. 

Start using Cameralyze now!

For more information on the topics mentioned in the article, such as object detection, human detection, face recognition, security, or Edge, please see the content below:

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