Whether or not our society likes it, surveillance cameras are everywhere.
From cities, and businesses to public transport, airports and shopping centres, the infrastructure of today’s society depends on security cameras and CCTV.
In the UK alone, there are predicted to be around 7.5 cameras for every 100 people in the country.
You’d think this would mean better security. But in reality, more footage doesn’t automatically lead to more protection.
Yet due to the sheer volume of video, it’s created the opposite outcome in that there’s too much to watch, not enough insight.
The traditional CCTV model with rows of monitors and human operators watching passively for hours, simply can’t keep up.
As security threats grow widespread, the cracks in outdated surveillance approaches are clear to see. For experts and for newcomers to the surveillance industry alike.
Why has the surveillance scale problem become unmanageable?
Modern CCTV systems generate a staggering amount of footage and nowadays it’s unsurprising for large organizations to have hundreds of cameras running around the clock.
Each capturing one of these cameras will be capturing video every second of the day, across a single city, transport network or even just a small shopping center, that adds up to terabytes of data daily.
Let’s take this example:
In a system with 100 cameras running at an average of 15 frames per second, you’re generating almost 130 million frames every single day. That’s 130 million moments to process, interpret, and assess – in real time. No human brain can keep up with that. And no conventional system can truly scale to that level of vigilance without drowning in data. That’s where anomaly detection changes everything.
Instead of trying to watch every frame, it acts as a filter – distilling the ocean of footage into the rare moments that truly matter. Less than 1% of the data is flagged as anomalous. And suddenly, it becomes manageable.
Now your team can focus on context and response – not just endless watching.
Because security shouldn’t be about watching everything. It should be about noticing what’s different.
Yet despite all this recording, only a small fraction of footage is ever reviewed – often only after an incident has occurred. This creates a major blind spot and it’s a frequent situation where security teams are just simply collecting everything, but seeing almost nothing.
Human operators can’t keep up
Control rooms are still built around human attention which is ultimately a costly, unsustainable way to analyze video in real time.
An operator might be responsible for 20, 50, even 100 feeds at once and no matter how good the team is, fatigue sets in.
Studies have shown that after just 20 minutes of watching video, operator detection accuracy drops significantly. The result is that critical activity can be completely missed simply because the operator’s attention is elsewhere. How many more times do we have to read this in the news: “an incident occurred under the camera’s eye, but no patrol was sent because the operator didn’t notice it”?
This leads to incidents slipping through the cracks and there are plenty of real-world cases highlight the issue:
- Theft that goes unnoticed because the camera was technically recording, but no one was watching.
- Trespassers captured on video but only identified long after they’ve left.
- Public safety threats that could’ve been spotted early if the right clip had been flagged in time.
This, however, isn’t a failure of people, it’s more a failure of process due to the fact that while the amount of surveillance cameras has scaled – how we monitor it hasn’t.
Understanding the hidden cost of false alarms in CCTV monitoring
Many legacy systems rely on motion detection or very simple rules to flag potential threats. The problem is, they trigger absolutely everything.
A cat walking past, a car’s headlights reflecting on a surface, objects blowing in the wind – all of these can cause an alert.
And when security teams are flooded with constant false alarms, they start tuning them out.
This can create complacency over time because ultimately once staff begin ignoring alarms or assuming they’re false, genuine threats are more likely to be missed.
It’s a natural reaction to unreliable systems, and it introduces serious risk – especially in environments like critical infrastructure, public venues, or retail spaces where a single lapse in judgment can have major consequences.
Every unnecessary response wastes time and money. Guards are dispatched, areas are checked, teams are pulled away from real tasks.
Multiply this across weeks or months, and it adds up to significant operational cost, and worse, it creates a culture where alerts are no longer trusted.
While no security system can be completely immune to false alarms, regular maintenance can significantly reduce their likelihood and make such incidents less frequent.
However, when managing hundreds and thousands of individual moments throughout a day, the issue isn’t one that’s going away soon if you continue operating like we have done for years on end.
Why AI and Automation are becoming a necessity for CCTV monitoring
Traditional surveillance depends on people watching screens for hours but attention quickly fades, and important events get missed.
Artificial intelligence solves that by doing what humans can’t: monitoring everything at the same required level all day and night.
Modern video analytics systems use AI and computer vision to scan live video feeds in real time. They automatically detect and track people, vehicles, behaviours, and objects that matter, while ignoring what doesn’t to cut down on false alarms.
Unlike humans, AI applies the same rules 24/7. It doesn’t lose focus or overlook something because say it’s the end of a long shift at work and attention spans are lowered.
Because these systems learn over time, they can be trained to ignore background movement, bad weather, or shadows – the things that often trigger false alerts in manual setups.
By filtering out the noise, AI helps security teams spend less time on routine footage and more time responding to real threats.
But there’s an even more dangerous side to this: the false negatives.
The event that never gets flagged. The threat that slips through unnoticed – even when basic AI is in place.
Take a typical intrusion detection setup. It might be good at spotting motion near a fence line. But what if the threat isn’t movement, but e.g. vandalism? A person calmly walks in, spray-paints a camera lens, or tampers with equipment. No erratic motion. No loud sound. And no alert – because no one configured the system to recognise anything beyond the basics.
This is where anomaly detection truly matters – the ability to spot what deviates from normal behaviour, even when it’s subtle or unexpected.
Legacy systems tend to miss what they weren’t specifically told to look for.
And in high-stakes environments, that blind spot isn’t just inconvenient – it’s a liability.
Humans still matter – just give them smarter tools
Automation isn’t about replacing people – it’s about giving them better information to act on.
Instead of sitting through hours of video or chasing every motion alert, operators can focus only on footage that’s been pre-verified and tagged with relevant data.
Give your team the ability to focus elsewhere on things that require true human insight/judgement and manage things appropriately.
They can search by time, location, object type, or event, making it easier to investigate, report, and respond faster.
Let AI handle the constant watching, while people step in with judgment and action when it counts. In short, machines monitor, humans decide.
Here at IntelexVision we’ve developed our tools to meet and pioneer the new age of monitoring with tools such as Sentry and Aurora are both designed to make this type of monitoring simple to deploy.
They work with existing camera infrastructure and operate in different environments and rather than replacing systems from scratch, they enhance what’s already in place.
These solutions focus on real-time detection, behaviour analysis, and low-latency alerts – all while learning and adapting over time.
We can appreciate we’re not the only platforms out there, but it’s a good example of how AI can be layered into existing surveillance setups to bridge the gap between raw footage and meaningful insight.
Ultimately, tapping into smart surveillance helps with:
- Fewer false positives and wasted callouts
- Faster reactions to real-time threats
- Operators who trust the system and stay engaged
- Easier compliance with regulations and audit trails
- Data-driven insights to refine future operations
- A drastic reduction in false negatives – critical events are no longer missed or ignored
It shifts your security setup from reactive to proactive, and over time, that’ll be what makes the difference.
Traditional CCTV isn’t broken – but it is outdated
CCTV has played a huge role in security for decades but the reality is that watching video manually no longer works at the scale we now expect.
Security threats are faster, more complex, and more varied than ever before. Meanwhile, footage volumes are growing too large for humans alone to manage. And this is before you begin to think about the increasing costs of both operators and systems.
By introducing AI-powered video analytics sooner rather than later, organizations can finally match the scale of their surveillance with tools that turn footage into useful intelligence.
It doesn’t mean ditching your entire setup, simply use technology that helps your people see what matters and when it matters.
