How to Manage the Video Data Tsunami with Smart Platforms

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System designers are regularly challenged by the exponential expansion of advanced sensor technology and how to implement them in a security or life safety environment. A network of sensors can hear, smell, and sense events such as audio alerts, gas leaks, and temperature fluctuations. However, it is video surveillance that has become the dominant force in security thanks to the emergence of intelligent cloud-based analytics that can tame and exploit huge stores of disparate data enabling actionable, real-time response. .

In a recent report, The physical security industry from 2020 to 2025, Global research firm Memoori said: “In 2019, Artificial Intelligence (AI) technology applied to CCTV convinced the market that by 2020 it will become mainstream within the next 10 years and that t is essential to take full advantage of the huge amounts of data generated by CCTV cameras and AI-based solutions are the only practical answer. Modern chip architecture with AI software can browse vast volumes of data and enhance security and safety. Granted, there’s a lot of development in this area that we haven’t seen yet, but the path to AI seems pretty clear.

The Memoori report added that “AI adds a brain to our ecosystems of smart technologies, allowing these systems to understand the wide range of sensory inputs and process massive amounts of data provided by sensor hardware. This is especially important for computer vision which feeds off many streams of CCTV footage and creates far more data than would be possible for human teams to monitor. Like the human brain, AI video analytics can count people, identify individuals, and recognize activities to drive safety, efficiency, and behavioral prediction applications, and even help us fight crime.

Migration from VMS to AI

The simple fact is that the security industry is moving from a traditional VMS to a sensor management platform for data collection. When talking to end users and technologists, migration is inevitable. The stress of data collection, data generation, and eventual data interpretation becomes more complex every day. A huge amount of data is created every day, approximately 80% of which is unstructured or “dark data”, while 37% is video data. This creates a heavy burden for people to locate, organize and enter data before the process even begins.

The challenge of transforming dark data into real-time actionable intelligence at the edge is the mission Fredrik Wallberg and the Airship AI technology team have embarked on. Wallberg, the vice president of marketing, says Airship has looked at the data and security landscape and what’s to come, noting that their three-letter agencies and client company create a ton of data but still have struggling to collect tangible information.

“We (security) moved away from live viewing almost all together. We’ve seen many reports of the Monkey that aren’t seen by people watching it live because they’re just out of date. So a lot of videos are being watched in playback mode,” says Wallberg, noting this significant trend away from live watching and exhibiting at ISC West 2022 this week in Las Vegas “What we’ve done through existing clients and new The large Fortune 100+ companies we work with bring deep expertise in ingesting existing data and metadata and extracting it from the video source to further enhance security and also enable businesses .

Wallberg says Airship AI’s ability to create a process that helps customers get real-time data visualization at the edge is a key differentiator. “Organizations do a lot of the computing and a lot of the data generation at the edge. Our AI Airship Outpost box is really just a small factory and video edge processing module that provides onboard AI data analysis and transmission.With these ever-expanding IoT sensors and devices that are connected to the Internet, we’ll likely see this as a trend in the future.We believe we’re at the forefront of technology when it comes to managing data points and workload at the time of data creation for maximum efficiency and accuracy.

What to do with all this data

Once the Airship AI process has regenerated and collected all of this data, the next step is data analysis. Wallberg says that’s when you bring it back into the core management platform so data can be ingested at the edge to organize and analyze, it turns all that dark data into actionable intelligence in time real.

“At this point, like many competitors, you need this powerful backend with at least a simplified user interface (UI) on the front-end to ensure that data silos are broken down and replaced with data hubs . The AI ​​engine can then see all structure or light data – if we call it the opposite of dark data – in the core,” adds Wallberg. “From an AI perspective, you create all of this data and you make sense of this data by aligning and connecting the backend system, merging and operationalizing the data where the visualization becomes reality. The last element is what we called data visualization in this type of data feed. And it’s really just providing insights into the whole data stream using AI. You’ve taken all these disparate systems, knocked down these data silos, brought IoT devices and sensors to the edge, ingested the data, analyzing it through core AI functionality, getting this real-time data visualization at the edge and then generate real-time notifications. So while you have a super-smart motor at the periphery and a strong motor at the core and rear, ultimately it has to be simplified.

The operational shift of the PSIM and VMS platforms is underway since artificial intelligence and machine learning options are gradually being accepted for several security systems. Of course, video surveillance remains the most visible example. Airship AI says its approach goes beyond basic video management software to develop a proactive, comprehensive “lens-to-server” solution that scales monitoring from a single stream to thousands of streams.

“If you look at a lot of successful big data-type companies, whether it’s Facebook, Amazon, or Google, they’re trying to figure out what they can do with all that dark, unstructured data. There’s a ton of data in the security world and we’ve spent a ton of time trying to identify where the incident happened, tightening up video, cutting bandwidth, just trying to reduce the amount of what we call junk data,” says Wallberg. “What we’re saying is it’s actually quite the opposite. With all the data generated, you can really start to understand what’s going on, not only from a business activation perspective, but also augment your security with a proper AI engine. You will be able to analyze data and begin to understand certain patterns. »

About the Author: Steve Lasky is a 34-year security industry veteran and award-winning journalist. He is the editorial director of the Endeavor Business Media Security Group, which includes Security Technology Executive, Security Business and Locksmith Ledger International magazines and the premier web portal SecurityInfoWatch.com. Steve can be contacted at [email protected]


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