AI Surveillance Solutions for Public Spaces

This detailed resource explains how AI-driven video analytics can help security teams identify risks, prevent incidents, and investigate events more effectively. It explores how metadata, deep learning, and integrated AI engines can be applied in real time or retrospectively to detect suspicious behaviour, identify high-risk objects, and manage crowd movement. The guide also covers best practices for unifying security and surveillance solutions, automating alerts, and creating workflows that enable faster, more accurate responses.

It offers practical insights for public safety directors, CCTV managers, and technology leads seeking to modernise their surveillance capabilities. You will learn how AI can streamline forensic investigations, support crime prevention planning, and help meet privacy obligations through anonymisation and responsible use of facial recognition. With real-world examples, technical guidance, and actionable strategies, it shows how to deploy AI responsibly to build safer, more secure, and more trusted public environments.

Download your free copy of the eBook 'AI Surveillance Solutions for Public Spaces'

Contents

• Introduction
• What is Meant by AI in a Surveillance Context?
• The Foundation of AI: Understanding Metadata
• The Tech: What You Need (and What You Need to Know)
• Applications: Using AI in the Real World
• Safeguarding Privacy with AI
• Conclusion
• Frequently Asked Questions

Introduction

Wherever large groups of people gather – from towns and cities, hospitals, and transport hubs, to campuses and major event venues – surveillance solutions play a vital role in identifying risks, preventing harm, and investigating incidents.

Continuously monitoring them is a massive undertaking that requires significant manpower. It’s also prone to error. Teams tasked with monitoring hundreds of video feeds can suffer from observational fatigue, leading to missed items and slow response times.

This guide looks at how AI can help solve this problem. Specifically, how AI-driven video surveillance analytics can be used – proactively and forensically – to protect members of the public.

The global Artificial Intelligence (AI) video analytics market is expected to exceed $75.35 billion by 2028, as a diverse range of sectors increasingly leverage the technology to advance their surveillance capabilities¹.

¹ https://www.secureredact.ai/articles/cctv-body-worn-cameras-video-analytics-retail-gdpr

What is Meant by AI in a Surveillance Context?

AI has become a complex term, but at a basic level, it is ‘a field which combines computer science and robust datasets to enable problem-solving²’.

In a public-space surveillance context, the datasets typically consist of camera footage. Data from a wider range of integrated sensors and systems can also be included, but this guide will primarily focus on AI-based video analytics.

AI software or firmware loaded onto devices uses complex algorithms to process and analyse images against these datasets to ‘recognise’ people, vehicles, shapes, specific event scenarios, and behaviours – applying intelligence to captured footage. Modern AI algorithms are constantly evolving based on what they see and on the data they generate, which is why you will often hear this process referred to as ‘video surveillance with generative AI’.

AI algorithms can be applied:

1. At the edge, where cameras feature onboard AI capabilities
2. Natively within a video management system (on premises or cloud-based)
3. Using integrated AI engines (on-premises or cloud-based)
4. Or involve a mixture of all three

² https://www.ibm.com/topics/artificial-intelligence

The Foundation of AI: Understanding Metadata

Wherever content analysis takes place, the key to successful AI implementation is always metadata.

Machines can’t watch and understand video footage as a human would. But they can be taught to classify and describe the information captured by surveillance cameras. This information is known as metadata. It is essentially what turns unstructured video content into recognisable and actionable information.

Examples of metadata in video surveillance

• Location 
• Time 
• Colour 
• Size 
• Speed
• Direction of Travel 
• Shape
• Volume 
• Dwell Time

Going granular with deep learning

Deep learning is a term used to describe AI models that process data in ways that mimic the human brain. As the name suggests, these models can be used to add a layer of deeper learning to metadata descriptions, making them more precise and granular.

The Tech: What You Need (and What You Need to Know)

This section looks at the specific solutions that leverage AI, turning metadata into actionable intelligence, and what you need to know about using them.

AI-enabled cameras

This description typically refers to IP cameras where AI processing occurs within the camera as a stand-alone function. Previously, these types of cameras were relatively limited in their capabilities due to the processing levels required.

But this has changed. Camera vendors have adopted the latest generation AI-enabled chipsets from manufacturers such as Ambarella. These advanced chipsets have led to enhanced deep learning functionality ‘at the edge’, meaning many cameras can now apply accurate real-time analysis to the scenes they capture. Estimates suggest that 30% of cameras sold in 2024 can embed deep learning.

Video Management Systems

Many Video Management Systems (VMS) now offer built-in AI capabilities as standard. The key benefit of using AI here is that, in addition to analysing live video data, AI can be applied to recorded footage.

This is particularly important for using AI as a forensic tool for investigative purposes.

Integrated AI engines

As the name suggests, this term refers to a dedicated AI engine that is separate from but integrates with your surveillance solution to analyse live and recorded footage. Your VMS provider may offer this as a bolt-on to your core solution (meaning you don’t have to replace your existing VMS to benefit) or provide integrations with a wide variety of third-party AI solutions, ensuring the customer has the freedom to select the best-of-breed AI for their specific application.

Rules, alerts and workflows

AI-based video analytics solutions may recognise what is happening in a particular video frame, e.g. a car is moving from left to right. However, they don’t know what you need to know unless you tell them. Make sure your VMS allows you to create and apply rules to specific types of events detected, which you can then alarm and use to trigger workflows – manual, automated or a combination of both. This ensures the right responsive action is taken.

Applications: Using AI in the Real World

This section examines specific use cases for AI-driven video surveillance, breaking them down into three clear categories.

1. Improving real-time risk and crime detection

High-risk object/weapon detection

Just as deep learning can be used to train AI to spot and categorise different shapes, it can also be used to spot and categorise different combinations of shapes. For example, ‘person carrying a backpack’ or ‘arm holding a knife’.

Rapid crowd formation/unusual foot traffic

AI can detect when the number of people in a specific area grows beyond the norm in a given time frame. It can also detect when the direction of foot traffic is ‘not the norm’ e.g. people running out of an entrance. This could indicate that a security or safety incident is occurring, or that there is a problem preventing the normal flow of people in a given area.

Suspicious/threatening behaviour detection

A cyclist riding in the bike lane is not suspicious. A cyclist weaving in and out of people on a pavement in a random pattern could indicate intent to snatch a bag. AI can be taught to recognise patterns like this, including signs of aggression linked to body movements.

Real-time alerts + automation

In each of these use cases, the threat detected can be used to trigger a specific workflow for the risk detected. This guides operators to verify threat, notify police and dispatch specific personnel. It will also automate certain actions, such as object/person tracking, launch pre-recorded evacuation messages, lockdowns, and switch to emergency lighting.

This capability has distinct implications for areas such as terrorist threat risk mitigation. With the imminent arrival of legislation like the UK Terrorism Act, using AI in this way could become more commonplace.

2. Your new forensic scientist

AI isn’t just good for instant alerts. Applying detailed metadata to video content makes it highly sortable and searchable. This offers a powerful forensic tool for rapid incident investigation. Here are some examples:

Missing person tracking

A child goes missing in a busy shopping centre wearing a red jumper and a blue hat. With AI, operators can ask the system to rapidly scan all footage using height, clothing type and clothing colour filters to show all matches as interactive thumbnails.

Person of interest search

Following a suspected theft, operators could rapidly search footage for a man, last seen on ‘Example Street’, wearing a green jacket and carrying a bag.
Person of interest searches may also involve use of facial detection/recognition – see section on ‘Safeguarding Privacy’ for more on this.

Law/rule enforcement

Litter being thrown from a car. Items left in a known fly-tipping area. Cars driving in bus lanes or jumping red lights. AI can learn what metadata combinations signify these events, so footage can be quickly searched for occurrences.

Forensic search + automation

In these cases, the primary value of automation is speed. AI can complete a review process in seconds that might take operators minutes/hours/days to complete.

Workflows and automation can also be used here, as with real-time alerts, to ensure next steps are efficient and protocol-compliant. There is also potential administrative resource value, for example, in automating the issuance of fines for law breaches.

3. AI-driven planning

Because AI enables insights to be gleaned from footage and categorises incident types, it is a valuable tool for reporting and planning. Here are some examples:

Incident cause analysis

AI is used to analyse footage from numerous incidents to identify root cause commonalities – such as specific event combinations that typically result in x. This can help facilities better understand, for example, the causes of slips and trips or specific types of workplace accidents.

Traffic improvements

AI-driven video analytics can be used to identify traffic patterns over a specific period, e.g., congestion hot spots by time of day or roads with an unusually high number of incidents. This information can then be used to inform new layouts, signage, speed limits etc. for safer, smoother traffic flow.

Crime prevention

Where might additional lighting prevent more assaults? Which areas would benefit from increased security guard or police presence? Questions like these are increasingly answered by generating reports from AI-driven surveillance analysis.

Safeguarding Privacy With AI

Safeguarding public privacy is always a priority. This section examines how this is achieved by using AI, and looks at some key considerations around one of the more controversial areas of public space surveillance – facial recognition.

AI used for anonymisation

AI can be a very useful solution for protecting public privacy thanks to its ability to differentiate between and categorise different shapes.

This capability means AI can be trained to spot and redact (blurring or completely covering) specific details in video footage, such as faces, bodies, vehicles, and license plates, depending on the requirement. It can automatically anonymise footage. This can be done in real-time or applied to video retrospectively.

Real-time facial blurring is a key trend, becoming increasingly commonplace for securing sensitive environments such as hospitals.

Three key benefits of video redaction/anonymisation:

1. Data compliance: Especially useful for rapid, data-compliant responses to Data Subject Access Requests (DSARs) and Freedom of Information Act enquiries (FOIAs).
2. Responsible evidence sharing: Organisations can easily share evidence with third parties, such as the police or insurers, while protecting privacy.
3. Public trust: Using AI in this way can help build public trust in the use of surveillance.

Face detection vs facial recognition

AI-based face detection solutions use algorithms to analyse the shape of a face in a video image to detect the presence of a human face. They can also be used to distinguish between typically male/female feature characteristics, etc. They do not identify that a specific person is present and do not match what they ‘see’ with a source image.

AI-based facial recognition (FR) uses more granular metadata, allowing very detailed facial characteristics to be identified and matched against source image metadata to identify a specific person.

Used responsibly in public environments, facial recognition has many potential advantages – for instance, searching for high-risk watch list individuals and supporting police investigations. In fact, the Defence and Security Accelerator (DASA) in the UK is currently exploring solutions to support the use of facial recognition technologies by policing and other security stakeholders.

But for those organisations concerned about adopting FR, the distinction between the two concepts is important. For example, both facial detection and facial recognition “could” be used to support a request to share video of a crime with innocent parties masked out.

Facial Detection requires more manual intervention, allowing an operator to selectively retain or redact individuals from the scene, whereas Facial Recognition simply requires the operator to have a matching image of the perpetrator for the FR engine to automatically match and redact all other faces.

Do you know?
Most AI engines for video surveillance applications feature some form of facial detection and recognition capability, but allow this to be configured on/off as required or even restrict its use based on the region of sale.

Conclusion

With AI-powered surveillance systems, patterns, anomalies, objects, and behaviours that suggest risk can be detected in real-time, supporting early intervention and rapid response.

Footage can be searched easily and intuitively, saving precious time when kick-starting post-event investigations.

Trends and insights that would otherwise be virtually impossible to spot can be identified, enabling proactive implementation of improvements that will make a difference to public safety.

All of which doesn’t replace human experience. It empowers it. Improving the ability of surveillance teams and law enforcement to detect and respond effectively to incidents, and to deploy resources efficiently.

Responsible use is crucial, with careful consideration of privacy and ethical implications. Especially when it comes to building public trust. But with the right solutions, organisations can leverage this technology to create safer, more secure environments.

Frequently Asked Questions

Given that AI can be deployed in various ways through different solutions, here are some common questions about best-practice adoption from a technical perspective.

1 Should AI-enabled cameras be at least 4K?

It depends on the application – for instance, if you simply need to detect movement, 4K may be unnecessary. However, the finer detail a camera can capture, the more intelligence AI algorithms can apply to the image, supporting a much wider range of use cases. For this reason, you will tend to find that cameras enabled with deep learning capabilities are higher resolution. It should be noted, however, that some high-resolution cameras use significant computational power to encode video, which may leave less of the camera’s chipset available for AI.

2 Do I always need to have AI-enabled cameras to benefit from AI?

No, you don’t. Video Management Systems with onboard capabilities and AI engines are often compatible with standard IP cameras, meaning analytics can be run in real-time and retrospectively centrally rather than at the edge. However, for sophisticated analysis, you are likely to get the best results from pairing AI-enabled cameras with these other solutions.

3 Are there any benefits to using cloud-based surveillance to leverage AI?

A cloud-based VMS will give you easy access to a wider variety of AI solutions, which you can integrate as needed. However, the required processing level makes cloud hosting expensive. It may also be problematic for real-time alerts, as you could suffer delays. A cloud-based solution can work well if you only need AI to analyse video retrospectively.

4 Are there any particular standards I should be aware of?

The most important one when it comes to AI is to check that your solution is compliant with ONVIF Profile M. This “standardises the handling of analytics and metadata between cameras, VMS and software platform, reducing the complexity of pairing products from different manufacturers”. It’s important to note that Profile M also covers cloud and server-based analytics.