These days, artificial intelligence (AI) is ubiquitous. Programs like ChatGPT are taking the world by storm, highlighting the demand for AI-driven capabilities across a wide range of industries.
But for those in the security space, AI in the form of video analytics has long been a reality. In fact, as analytics become more sophisticated, cameras powered by machine learning (ML) and deep learning (DL) technologies have become commonplace, giving organizations around the world powerful new capabilities. .
These new capabilities help organizations keep their locations and assets safer than ever, but organizations are now leveraging them in ways that go way beyond security. Today’s business leaders are using AI-driven analytics to improve and automate security processes, generating valuable business intelligence insights that can be used to increase efficiency, streamline operations, and increase revenue.
Modern Advancements in AI and Analytics
It cannot be overstated how advanced video surveillance technology has come in a relatively short period of time. Not long ago, cameras were primarily reactive technology used to record video for later review. (When most of us think of video surveillance, we still think of the lone security guard staring at a wall monitor.)
[ Also read Security automation: 3 key benefits. ]
Modern solutions have come a long way since then. Today’s cameras no longer rely on human operators to recognize suspicious incidents as they occur. Thanks to AI, organizations can deploy a wide range of analytics capabilities to automatically detect and alert on potential security events.
That said, it’s important to set the right expectations for video analytics and artificial intelligence. AI, ML, and DL have made exciting new things possible, but it’s important not to get caught up in the hype. Before deploying the latest and greatest technologies, it is important to understand how to use them properly and effectively.
For example, at the beginning of the COVID-19 pandemic, many organizations purchased thermal cameras thinking they could be used for fever detection. However, thermal cameras were not well suited for that purpose and many customers were unhappy with the technology.
Even as AI becomes more powerful, it’s important to remember these lessons.
Impact of AI on security
Processing at the edge greatly reduces bandwidth and storage needs, making AI-based monitoring tools more affordable and accessible.
One of the most important developments in AI analytics is the advent of deep learning processing units (DLPUs) and subsequent integration capabilities at the edge. These advanced new chipsets enable cameras to process vast amounts of information at the network edge, enabling organizations to perform analytics natively on the devices themselves. This had a significant impact on the overall cost of analytics, as organizations no longer needed to send video feeds to the cloud for analytics. Processing at the network edge dramatically reduces bandwidth and storage needs, making AI-based monitoring tools more affordable and accessible to organizations of all sizes.
The camera has also been improved, with better resolution, better image quality, and better light sensitivity. This allows AI to work with sharper images, improving accuracy when recognizing and classifying objects.
As DLPUs and advanced object detection become the norm, organizations will be able to perform analytics such as license plate recognition, loitering detection, and gunshot detection. It also makes common features like intruder detection more reliable as the camera learns how to filter shadows and distinguish between humans. , Shiga.
AI can also automate the detection and alerting process, allowing security personnel to be notified of potential incidents in progress in real time, improving response times and enabling security teams to be proactive before problems escalate. will be able to respond to incidents as quickly as possible.
Apply AI to your business needs
In addition to security, AI-driven analytics are now being applied to core business functions. For example, retailers use analytics to understand all kinds of customer activity, such as when customers are most likely to visit a store, which displays get the most attention, and how they navigate through the store. Tracking. This allows you to deploy staff more effectively, measure the success of your display and marketing campaigns, and improve operational efficiency by eliminating points of failure.
Manufacturers can use the same technology to identify defective products on the assembly line or detect signs that machinery needs maintenance. The hospital can enforce proper use of his PPE and prevent costly slip/trip/fall cases. Across a wide range of industries, AI-enabled cameras can help companies improve operational efficiency and generate a positive ROI for him.
For business and technology leaders, this creates a unique opportunity to extract more value from their security investments. The same devices already used for security purposes can now be used to generate positive ROI for her marketing and business intelligence departments, improve workplace safety and reduce liability.
Companies across all industries are extracting more value from their surveillance devices, even as the overall cost of ownership is reduced due to increased video compression and edge processing power. From a security and business perspective, there has never been a better time to invest in video surveillance.
AI utilization in the future
The world of video surveillance is approaching a tipping point. Once used only for security purposes, cameras are seeing exponential growth in business applications. The growth of AI and accessibility are key drivers, enabling enterprises to leverage the same technology to generate new business insights while dramatically improving their security capabilities.
As AI continues to evolve, organizations today need to make the most of their devices as both security solutions and business enablers.
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