Video Analytics: The What’s and the How’s

Video surveillance systems are an essential part of many businesses and industries, but without the right software it can be difficult to get the most out of your camera’s footage. In order to get the most bang for your buck, it’s important to choose a video analytics software that you can integrate into your existing video surveillance system to help you understand and act on the information presented in your footage. The following guide will help you choose the right video analytics software for your business or industry.

Why do you need video analytics?

In most business environments, there is only a small amount of information to be gleaned from analyzing video footage. However, with intelligent video analytics software, you can gather intelligence about both what is happening in an environment as well as who is interacting with it. Now that businesses have access to software that can recognize people and behaviors, they have new opportunities for applications ranging from retail to sports to law enforcement. For example, an intelligent system could detect if a person enters a store repeatedly and then leaves without purchasing anything; such actions would indicate potential theft on their part. By providing insights into potential problems before they happen or after they occur, you’ll be able to better protect your business—and help deter criminal activity too. 

Intelligent Video Analytics Software

First, there was video surveillance software. Then came intelligent video analytics software. And now, there's cognitive video analytics software. So, what is it? Simply put, cognitive video analytics is an advancement in our ability to process videos quickly. Cognitive processing can be thought of as learning algorithms built into a system that allows it to learn patterns over time – making more accurate decisions based on those patterns with each pass. This makes cognitive video monitoring very effective at preventing security threats while reducing false positives by constantly improving itself as time goes on...all without human intervention!

Video Analytics Applications

There are many applications for video analytics, from event detection to crowd density measurement. For example, you can use them to count people who walk through doorways, turn left or right in hallways, or stop at kiosks. They can measure in real time how many people are waiting on line at a bank teller or a coffee shop counter. They can detect moving objects such as cars and pedestrians, monitor employee traffic flows during company break rooms throughout an office building during lunch hours. They can even be used to calculate space utilization within a warehouse by tracking how much space is being taken up by merchandise that is shelved. In short, there is no limit to what these systems can do.

Benefits of Video Analytics

Video analytics are a way of analyzing video content by applying computer vision algorithms to automatically detect, recognize, and track events in a given video stream. One of their major benefits is that they provide an objective method for quantifying activity in security videos, compared to human observation. Video analytics also allow organizations to perform risk assessments without relying on assumptions or estimates that may become outdated over time. Most systems are fully automatic, requiring little human intervention once they’re up and running. This means less work for security personnel but it can also lead to more false positives.

Limitations of Video Analytics Software

Although video analytics software can be very powerful in allowing you to search through large volumes of footage, there are some limitations that you should be aware of before you begin to implement them.

·          First, you will need a well-lit, high-quality camera for every corner of your building or office if you want to capture all potential activity.

·          Second, it's important to note that video analytics software cannot actually control your cameras.

If someone is tampering with one of your cameras at any time during your surveillance period, then an alarm won't go off on screen—and could potentially put yourself or others in danger. Video analytics software may be effective for general security measures but does not serve as a perfect replacement for other forms of security monitoring equipment.

Conclusion

Now that you know about video analytics, it's time to start thinking about how you want to make use of them. There are endless possibilities when it comes to incorporating video analytics into your company's operations. Video analytics could be used as part of a security system in your lobby or even just sitting on your dashboard, recording everything going on around you while you drive.

Depending on what type of business or organization you're working for, they might have specific needs or wants regarding video analytics. Before implementing any new software into your existing set-up, be sure to discuss all requirements with IT staff so they can find the perfect solution for both their needs and yours!

What is the algorithm used in video surveillance

 Video surveillance has existed for years but it is now used more than ever. From the corner of the streets to the elevator of buildings, we are watched everywhere. A lot of factors contribute to the rising popularity of video surveillance.

Apart from fast internet, development in technology, and better devices, algorithms have a huge part to play in it. With the introduction of AI-based video surveillance systems, things have become fairly simple for everyone. Machine learning and see learning are some other technologies that are highly impactful when it comes to video surveillance.

Back in the day, people complained that video surveillance was unable to detect the right objects or often reported false alarms. However, this is not the case now as the algorithms have improved and the techniques have advanced. 

How Algorithms Can Enhance Video Surveillance Systems?

AI-Based Video Surveillance

Artificial Intelligence is a great addition to video surveillance as it can identify humans, objects, and even different situations. It analyses the footage to go through the audio as well as video carefully. Such video surveillance systems can be fed with data to make the software realize which situations are normal and which are fishy.

Once the software captures something that is not normal, it sends out alerts immediately to warn the security team. Since AI never gets bored or has a short attention span, it can track every movement to ensure optimum security. Undoubtedly, AI tools can increase the efficiency of any organization.

Machine Learning

Machine learning is all about collecting a wide array of data and then training models according to this available data. This technology has brought some breakthrough results in the field of video surveillance. With machine learning, the accuracy of video systems leveled up.

Apart from accuracy, the ability to distinguish between different objects increased. Objects like cars, different animals can now be identified easily. Also, sounds of different frequencies are identifiable with the help of this algorithm. Most critical problems were solved with the arrival of this technology that led to the betterment of video surveillance systems.

Deep Learning Algorithms

Deep learning is a branch of machine learning. This technology is a family of algorithms and unlike machine learning, it is not complex. Moreover, the software just needs to be trained using this algorithm by collecting different images.

Since it can eliminate the never-ending problem of false alarms, deep learning may just prove to be the most efficient strategy for video surveillance. In simple words, deep learning mimics the mechanism of the human brain to process information. Plus, algorithms based on this technology are exceptional as they allow you to train or control the system.

Increased Efficiency

The security of an organization can be constantly increased using deep learning algorithms. Since the models can be updated continuously, more data can be collected. This process can be repeated till the results are satisfying enough. Alongside, the more data is fed to the system, the more it will understand the human environment.

High accuracy can now be expected out of video surveillance systems because of these factors. Most computer algorithms have an accuracy rate of 95% but this is not the case with deep learning as it offers an accuracy as high as 99%.

Solving Major Issues

When it comes to face or object identification then deep learning’s improvement is unstoppable in this area. Most facial recognition applications are based on the algorithms of deep learning. Apart from his accuracy and identification of objects, one of the greatest advantages of this technology is that it can figure out abnormal events.

Unexpected events can be easily spotted by this tech so it will drastically reduce the cases of false alarms. Several people who were hesitant of opting for video surveillance systems because of the false alarms factor are now open to this idea of AI-based surveillance. 

Behavioral Analysis

AI-based surveillance systems can now understand the behavior of humans and determine if the situation is normal or not. Features like size, speed, actions, color, and orientation contribute to figuring out the behavior of humans. By tracking the patterns and matching them with the data helps the software in realizing whether the condition is alarming.

To Conclude - 

Deep learning technology is being rapidly opted by various industries as it offers various benefits. Especially when it comes to video surveillance, this technology has proved to be a game-changer. Several solutions popped out by simply scratching the surface of deep learning technology so it has a lot in store for the future.