Getting Smarter About AI

Surveillance technology has change with new breakthroughs and enhanced remote monitoring

The past few years have seen companies throughout the security and surveillance industry expand their use of AI, some rapidly adopting the technology and others dipping their toes in the water. Either way, AI has certainly moved beyond being an emerging technology to now being a proven reality, with demonstrated abilities to improve security camera imaging performance, enhance the accuracy of people and object detection, reduce false alarms and conserve recording and network bandwidth.

Surveillance technology has changed dramatically over the last few years with new breakthroughs in digital imaging and optics, enhanced remote monitoring capabilities, and more efficient methods of data storage and bandwidth management. The continued evolution of innovative solutions combining AI with on-board audio and video analytics is resulting in highly accurate object detection and classification, with fewer false alarms, plus the benefit of actionable data that can drive intelligent monitoring to enhance operational efficiency.

In 2024 and beyond, it does not take a tremendous amount of foresight to predict that AI will grow in use in every surveillance application across a range of markets. Also, AI is not a technology that operates in a vacuum; it affects several other types of surveillance technologies, including cloud-managed services and the use of data analytics.

With that in mind, the following are some of the more critical areas to keep in mind throughout the year and beyond:

Generative AI vs. machine learning
AI has become a ubiquitous term for any robotic assistant or automation technology. But there are really two different areas. Machine learning is a subset of AI and refers to the ability of “machines” to learn over time. For example, think of when Netflix recommends other movies you might like, based on its analysis of your previous viewing habits.

Then there is Generative AI which has similar characteristics to machine learning, but also can “creatively” develop original content, ideas and images. In this case, think of Chat GPT or any of the open AI platforms coming to market.

In the surveillance world, generative AI can be used when collecting a dataset is too time-consuming or might include personal data. Generating a realistic yet graphical image with multiple variations like low light, snow or rain is much easier, and could become the basis for training AI models.

For example, in license plate recognition there are multiple variations like language, format and specialty license plates, and getting a real image of every single variation is almost impossible. Rather, if we were able to generate a graphical yet realistic license plate image with any variation. Now, we have a smarter AI model that can detect these variations without the need to collect datasets from each country and state.

This may become a larger trend as more companies rely on AI models to train their devices. For example, Hanwha Vision’s FLEX AI has the capability to convert any static object into data that Hanwha's AI cameras can comprehend. We have heard from our customers that they require unique solutions to address their specific business challenges. This often involves the need to train custom objects not included in off-the-shelf AI cameras. FLEX AI is a cloud-based application that allows users to train objects with as few as 20 images.

AI Driving New Efficiencies
The continued integration of AI and Machine Learning in security and surveillance systems has enabled smart video analytics, automated threat detection and predictive analysis.

Depending on the environment and activity in surrounding areas, an organization may need to consider the factor of “unnecessary information” when it comes to AI analytics to detect and track motion. A security team only needs to know if something comes over the perimeter or enters the grounds, but the elements on the outside usually are not that useful. For example, if there are many waving trees along a perimeter or surrounding a building, then those environmental factors may trigger false alarms.

Combining AI into video surveillance systems leads to fewer false alarms and more effective and accurate forensic searches effectiveness. AI also enhances bandwidth reduction algorithms as we see a heightened need for more accurate AI-based detection of people, objects and vehicles. Previously technologies focused on pixel changes created by any type of motion: rain, snow or video noise, which could cause video bandwidth to increase. Now, all these pixel changes are ignored to focus only on what users need to see. AI is also effective to reduce video noise and motion blur.

Heavily wooded areas versus open areas could also be factors in determining context. Often, security teams do not care much about trees swaying in the wind since they are more focused on the fence line or only want to see people walking inside the perimeter.

AI-based “’smart” compression and noise reduction technologies are growing in use. They can say to a user “Here’s this high-resolution camera footage, do you want to reduce bandwidth and manipulate the compression based on the objects we care about?”

This level of context awareness takes AI beyond the level of pre-configured algorithms. It lets a system gather information about its environment and adapt its behavior accordingly. Now, the camera is making choices to optimize its performance based on what it has learned in the past and what is important to the user.

The coming months should also see a rise in the use of AI across many vertical sectors,

Schools. Digital imaging surveillance technology combining AI with on-board audio and video analytics can help school administrators get a better handle on access control and monitoring of hallways, classrooms and exterior parking lots. For example, knowing which doors visitors can access and exit the building is important when placing cameras. These analytics deliver actionable data that can drive intelligent monitoring for education facilities, helping administrators get a better handle on access control and monitoring of hallways, classrooms and exterior parking lots.

Healthcare. The use of AI is spreading across the healthcare industry to enhance patient care, improve operational efficiency and even contribute to medical research. From a security and surveillance perspective, hospitals are complementing their cameras’ security monitoring performance with enhanced data-gathering capabilities combining intelligent audio/video analytics and AI.

With so many feeds to monitor across the different areas of a hospital, putting a security person in front of a video wall is not practical since an individual is realistically only able to monitor 10 to 20 cameras at a time. Hospitals are using AI combined with video analytics to help manage their networks of cameras and devices, shifting their security and surveillance approach from reactive to proactive. The result is targeted object detection and classification, which can save time for hospital security teams by speeding forensic searches. When an incident occurs, locating a person of interest can take a matter of minutes instead of having to spend hours sifting through hundreds of camera streams.

AI also is playing a larger role in cameras used for license plate recognition, recording vehicle entry and exit, and alerting staff to potentially dangerous activities in real-time.

Retail. New AI technology has added the power to do people-counting, body temperature detection, object detection, license plate recognition, behavioral observations and any number of actionable business functions. These new video solutions are addressing the industry’s need for scalable and cost-effective surveillance solutions that can help organizations monitor their stores and detect suspicious activity, preventing theft.

Over the years, we have seen a growing shift in the retail industry away from traditional on-prem servers and NVRs towards cloud-based solutions. Retailers can now deploy self-contained systems while fully realizing the potential of edge storage, multi-camera recording, remote access and on-prem security system management. It is important for retailers to easily customize and grow their surveillance systems based on their specific needs.

Intelligent video surveillance and AI technology also helps retailers be proactive when it comes to in-store shrinkage.

With security and surveillance devices now increasingly being tasked to do more than just “monitor and protect,” comprehensive, AI-powered intelligent technologies are now regarded as 360-degree, total business transformation solutions.

This article originally appeared in the March / April 2024 issue of Security Today.


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