BlogHow Deep Learning Algorithms Are Used in Video Analytics

In 2006, the term "deep learning" was introduced, and since then, it has been a fascinating topic in the world of artificial intelligence. Even though it seems that technology is encroaching into all areas of our lives...

From the way we watch a movie, to the method we pay for our groceries, it's not all negative. Deep learning and video analytics are one of the best ways to monitor street activity and keep track of suspicious movements.


Because deep learning allows us to get in-depth knowledge about human behavioral patterns, and program software to react and gather the appropriate data.

Now, if you're wondering what role deep learning algorithms have in the function of video analytics, keep reading.

Understanding Deep Learning Algorithms

Without getting too technical and writing a bunch of words that make no sense, it's important to understand what deep learning is.

Otherwise, it will be difficult to see why they've become popular in video analytics trends.

Deep learning is a category of machine learning which describes algorithms that have been created off the structure of a human brain. It's the way humans can teach technology to react and move like us.

Sounds pretty interesting, right?

Well, it gets even better!

When deep learning and video analytics work together, they can carry out tasks quicker and more efficiently than a human being could. Its complex systems allow faster output and better performance.

But, before we talk about how deep learning influences video analytics, here is more information on video analytics software.

Video Analytics

The easiest way to understand video analytics is to look at like as a tool to detect an incident that needs human attention and to warn people that an accident could happen shortly.

After receiving this information, security professionals and law enforcement can either act immediately, or record data to create better safety measures going forward.

CCTV cameras are the most common example of video analytics that people have heard about. As serious incidents like car accidents, and burglaries are often reported hours after they've happened...

With video analytics, you can have surveillance monitoring spaces throughout the whole day.

That way, you can focus on keeping people safe. As well as protecting society, you can also make sure other risks don't happen next time around.

Video Analytics and Deep Learning

So, let's take a look at how deep learning and video analytics come together. Deep learning is the component of artificial intelligence that learns how to identify and separate people and objects into different categories.

Then, this information is transferred to employees and the head of security. That way, the correct protocols can be introduced to solve a problem. Deep learning helps with the following:

  • Filtering through video footage for specific people and objects
  • Sending security alerts
  • Converting the data into charts for future security purposes

With this kind of software, law enforcement officials can react to situations in the moment and interfere when necessary. Plus, this information can be used to carry out post-investigations.

As well as helping law enforcement, governments can also utilize the information for traffic optimization and urban planning. Therefore, cities can have better and more secure roads.

Another situation when video analytics and deep learning are used in unison is in shopping centers. Here, the system triggers alerts to warn shop owners of thieves and to calculate the most popular times of the day.

Finally, airports and other large venues can benefit from both of these technologies. This software can observe how many people are in a crowd, highlight overly populated spaces, and improve traffic flow.

Even though deep learning is becoming more common with video analytics, there is still an exciting world to explore as the technology keeps evolving!

The Power of Deep Learning and Video Analytics

In the modern world, we are constantly excited about the next big thing. For algorithms, the anticipation is just as thrilling as with a new movie phone or household appliance.

Currently, there are ten common deep learning algorithms. Here is a list of them:

  • Convolutional Neural Networks (CNNs)
  • Long Short Term Memory Networks (LSTMs)
  • Recurrent Neural Networks (RNNs)
  • Generative Adversarial Networks (GANs)
  • Radial Basis Function Networks (RBFNs)
  • Multilayer Perceptrons (MLPs)
  • Self Organizing Maps (SOMs)
  • Deep Belief Networks (DBNs)
  • Restricted Boltzmann Machines (RBMs)
  • Autoencoders

All of these algorithms work with almost every type of data, but they require large amounts of computing power to function. That being said, there is always a new algorithm just around the corner.

So, this is just the beginning.

However, video analytics and deep learning will certainly replace the traditional security setup in the long term. The only differences we will see are the features and how they interact with humans.

As the surveillance world is constantly looking for detection features, smart search, business intelligence, and integrated solution automation, there are many areas to explore in this complicated system.

More businesses are going to be using things like object detection to secure their properties, and law enforcement will use video analytics to control traffic accidents.

Therefore, it's important to start using these systems as soon as possible. Otherwise, you could end up falling behind the rest of the world and losing out on valuable technology that can enhance your life.

In addition to creating safer communities, this technology is also a lot cheaper and can be conducted all over the world. There are no geographical boundaries stopping video analytics from helping people.

What Are the Challenges of Video Analytics?

Although there is an increase in the use of video analytics, that doesn't mean that are no obstacles getting in the way of this software.

For instance, as the world is becoming more cautious with surveillance, the volume of data increases. Therefore, data storage has become more of a concern for people working in this field of work.

Furthermore, the information gathered via video analytics is only useful if you know how to use it correctly. That means, that if your employees don't know how to use the data to its advantage, the software is pointless.

Educating employees on the importance of deep learning and video analytics is essential if you want this AI tool to be effective in the workplace, and society at large.

Finally, the most devasting challenge that affects video analytics is security breaches of data information. As people are hesitant about hackers stealing surveillance footage, there is a need for protection.

If you can secure the data retrieved through video analytics and make sure it's kept within an organization, there is no reason not to use this software.

Why Deep Learning and Video Analytics Are the Future

It's good to be aware of the benefits of deep learning and video analytics before you start using these systems in your workplace. After all, AI algorithms are still quite new to a lot of people.

Therefore, you want to know exactly what you're signing up for when you implement them, and how they are going to improve your life.

Increased Accuracy

Compared to other traditional video tools, deep learning used in modern video analytics provides detailed results and better accuracy. Technology can distinguish between objects and people with no problem.

The only concern in the past was that technology could never keep up. The good news is that this software can replicate human insight just like an employee.

This hyper-awareness can make spaces safer and create more welcoming environments for people. And, you won't have to constantly have an employee sitting looking at surveillance footage.

Smart Search Potential

GPUs combined with machine learning have been revolutionary in processing data. Whether you want to use video analytics in airports, YouTube video analytics, or other computer vision methods, this can save you a lot of time.

The tool works like a search bar where security professionals can type in a keyword and find video footage directly linked to a group of people, clothing, or objects.

More Safety in General

There's no denying that the main benefit of deep learning and video analytics is to increase security in general.

If this technology can help the world return to normal after the pandemic, it can only be a good thing. However, we need more than human knowledge to create safer environments.

We need detailed data, informative footage, and thorough research to create a better planet for everyone.

Use AI Technology to Create a Better World

Ultimately, deciding whether or not to use video analytics and deep learning is a personal choice. Although, as you can see from this article there are many advantages to switching to this technology.

You can get better information, improved security, and more access to data that can help you avoid accidents. If this software is used correctly, it has the power to shape societies and create safer worlds.

However, you need to learn how to use video analytics first...

Thankfully, that's where we come in. Interested in using video analytics? Get in contact to find out more today.

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