Understanding smart surveillance: Everything you need to know about video analytics

With surveillance cameras capturing endless footage across cities, businesses, and public spaces, the security industry has started to realise that even though we have more visual data than ever before, we’re getting less actionable intelligence from it all.

It’s unfathomable to think about just how many cameras there are worldwide – so much so it’s hard to find concrete data on the actual number.

In 2021, a Wall Street Journal article said that there’s over 1bn cameras across the globe  at the time of writing – and that has only increased since.

However crazy you might think it is, if you stop for a second and think, the surveillance overflow mentioned above isn’t just inefficient for businesses – it’s downright dangerous. 

Human operators simply cannot process the waves of footage generated daily, leaving critical security gaps that traditional CCTV systems can’t address. 

That’s why video analytics isn’t just a simple upgrade to existing systems – it’s become an absolute necessity for any organization serious about security, safety and overall operational efficiency.

Video analytics is essentially the technology that processes video footage using artificial intelligence and computer vision to automatically detect, track, and analyze objects, people, and behaviors. This is all primarily without human intervention. 

Unlike conventional surveillance that merely records for later review, video analytics interprets footage instantly, identifying what matters while filtering out irrelevant information.

Modern surveillance now incorporates an intelligence layer that takes all those endless visual feeds into structured, searchable data. 

This shift means surveillance has evolved from a purely forensic tool (reviewing what happened after incidents) into a proactive system capable of preventing problems before they escalate. 

Video analytics bridges the gap between having cameras everywhere and actually understanding what those cameras are seeing. 

In short, the days of security personnel staring at wall monitors hoping to catch issues are long done and dusted.

Video analytics greatly improves security, safety, and operations by providing smart, always-on monitoring.

The most obvious use case is for security as it helps detect threats like people crossing into restricted areas, loitering, or leaving suspicious packages. The technology can tell the difference between people, cars, and animals, reducing false alarms. 

It also recognizes license plates and faces to manage access and watches crowd behavior to spot possible conflicts – all with minimal supervision or mistakes.

In terms of health and safety, video analytics checks if workers are wearing proper safety gear, spots slips and falls, and detects fire or smoke early. All these scenarios are ones where legacy equipment might not spot automatically as it’s not been trained to and lead to potentially catastrophic incidents.

On the operations side, a be helps manage crowds and improve traffic flow. In stores, it tracks how people move, how long they stay in one spot, and how they interact with products. It also counts people to meet capacity rules and gathers data that helps with marketing.

Organizations can work more efficiently by automatically removing most of what can be considered “unimportant footage” to allow people only to look at what really matters. 

Also, this technology can spot threats early, before they turn into real problems which means you can proactively place teams and personnel exactly where they’re needed, saving time and resources. 

The system also helps make sure safety and security rules are being followed and, when something does happen, it’s easy to find the right video quickly using smart search tools.

The easiest way of explaining how it works is by three simple distinctions: input, processing and output.

It all begins with video streams from your existing camera infrastructure – whether that’s using fixed cameras, PTZ (pan-tilt-zoom) cameras, thermal imaging devices, or other recording devices.

One of the major advantages of modern video analytics is its ability to work with cameras you already have installed, meaning you can often leverage current investments rather than replacing entire systems.

These feeds provide the raw visual data that serves as the foundation for analysis, with higher resolution cameras typically enabling more accurate results.

This is where ordinary footage transforms into intelligent data and by using algorithms and neural networks, video analytics systems can:

This processing can happen at different points in your security architecture:

The most advanced systems employ deep learning, allowing them to continuously improve their accuracy based on feedback and new data. 

This element immediately is a clear improvement on the manual / semi-manual processes seen in outdated setups as it requires minimal human input yet still produce the same, if not better, results. 

The final stage delivers actionable intelligence in formats that security teams and operations managers can use immediately:

This transformation of raw video into structured data makes the difference between drowning in footage and making informed decisions.

Getting video analytics right isn’t about buying the most expensive system and hoping for the best – it starts with knowing exactly what you want to fix. 

Companies that see the best results first identify their biggest security headaches rather than just adding technology because it seems impressive. 

For example, if you’re trying to stop shoplifting in a large shopping mall, make sure workers are wearing the correct safety gear, or try to figure out why people are targeting certain stores/areas. Having a clear problem to solve helps you pick the right tools and know if they’re actually working/worthwhile.

It’s also good to start small instead of changing everything at once as by choosing just one area / problem that’s causing real pain, you can prove the technology works before spending big money on a company-wide rollout. 

You’ll also give your team time to learn how to use these new tools properly, work out any issues with your specific environment, and show skeptical managers real results that matter to them.

As surveillance systems generate ever-increasing volumes of footage, the gap between having cameras and having actual security intelligence widens for organizations still relying on traditional approaches.

Video analytics closes this gap, transforming passive recording into active intelligence that protects people, assets, and operations. 

It’s important to remember that this isn’t just a silver bullet, even the smartest AI can’t replace good human judgment and the best security setups combine what computers and people each do best. 

Computers never get tired, and can track video feeds 24/7 whereas security staff bring critical thinking, common sense, and an understanding of human behavior that machines simply don’t have.

Ultimately, only a human can tell the difference between someone who looks lost and someone acting suspicious (at least for now) but video analytics creates a much more proactive layer of security and safety.