Many brands have announced AI-powered security cameras. But, is it just a marketing hype or an actual game changer?
Artificial Intelligence (AI) is rapidly reshaping security camera technology, equipping surveillance systems with advanced capabilities—from facial recognition to predictive analytics. AI is not only redefining how cameras operate but also expanding the scope of real-time monitoring. The global market, valued at US$64.10 billion in 2023, is projected to reach US$209.10 billion by 2033, growing at a CAGR of 12.55% over the forecast period. Rising adoption of AI and deep learning is driving this growth, significantly enhancing the effectiveness of surveillance.
Is AI in security cameras just a marketing hype?
Given the surge in AI-powered camera announcements, it is natural to question whether the systems deliver on their promises. To summarise: while certain features remain in early development, the impact of AI in security is both tangible and significant. The ability to reduce false positives, automate surveillance tasks, and provide deeper insights into security data marks a major shift. However, as with any emerging technology, there are growing pains—AI models may occasionally underperform or require tuning. Still, the overall trend is clear: smarter and more efficient surveillance solutions are on the rise.
Edge vs cloud
The ‘edge versus cloud’ debate sits at the centre of AI-driven surveillance innovation. Some cameras process data directly on the device (edge AI), while others transmit footage to a central server for more complex analysis (cloud AI). Here is how the approaches differ:
Cloud AI
This traditional architecture is still widely used. In this setup, AI-powered servers operate in the cloud. Data from the cameras is transmitted to these remote servers for processing. While this allows for more powerful and scalable machine learning models, it also introduces latency and demands high-bandwidth internet connections. Cloud AI is particularly common in large-scale installations, where data from multiple cameras must be aggregated for pattern recognition and broader situational awareness.
Edge AI
Thanks to advancements in electronics and camera hardware, it is now possible to analyse data and make real-time decisions directly on the device. A true edge AI system equips cameras with the computing power to capture, process, and respond in real-time, eliminating the need to transmit data externally. This significantly reduces reaction time, though it results in higher device costs due to more sophisticated hardware.
Server-on-Edge AI
This emerging architecture blends the strengths of edge and cloud AI. In this model, the server is positioned close to the edge (i.e. near the cameras). This setup reduces latency and minimises bandwidth usage—critical for applications such as facial recognition, intruder alerts, and real-time anomaly detection. Server-on-Edge AI offers advantages such as faster processing, enhanced privacy, and greater reliability. The choice between architectures depends on the specific requirements of the security application.
| Advantages and challenges of Server-on-Edge AI | |
| Advantages | Challenges |
| Lower latency. AI servers process data locally, eliminating delays caused by cloud transmission Enhanced privacy. Sensitive data remains on-premises, reducing cybersecurity risks Cost-effectiveness. Avoids ongoing cloud subscription fees for video analysis Scalability. AI models can be tailored to specific security needs Reliability. Functions even during internet outages | Higher initial investment. Requires robust on-site hardware Regular maintenance. AI models need continuous updating to maintain accuracy Power consumption. Persistent AI inference demands significant energy |
What’s changed? Learning capabilities of AI security cameras
The introduction of edge-based AI has notably reduced response times for smart camera functions. Here are several capabilities that have become faster and more accurate with Server-on-Edge architecture:
- Real-time object detection. Instantly identifies threats
- Facial recognition. Matches faces against databases for access control
- Anomaly detection. Spots unusual activities to help prevent incidents
- License plate recognition. Tracks vehicle movement
- Crowd analysis. Supports public safety in high-density areas
- Behavioural understanding. Recognises actions such as loitering, tailgating, or sudden crowd formation
- Dynamic context recognition. Reads and interprets numbers or other information contextually
AI security cameras now actively analyse video feeds in real-time, using machine learning to distinguish between people, vehicles, and animals. This improves accuracy and reduces false alarms over time. Features such as custom object detection—as seen in solutions like Arlo Secure 5—and LPR systems add monitoring precision. These cameras can also interpret behavioural patterns to flag anomalies and integrate with broader security systems for automated responses, such as triggering alarms or locking doors. The result is more personalised, efficient, and proactive security solutions.
The power of numeric recognition
Number recognition is not new—optical character recognition has been around for decades. But AI is supercharging it. Cameras can now recognise handwritten or digital numbers in real time, even in poor lighting, odd angles, or through obstructions like glass or mesh.
This means security cameras can now:
- Track delivery IDs or locker codes as they are entered or displayed
- Read metre readings remotely (useful for utilities)
- Identify specific tags on uniforms, vehicles, or tools in industrial environments
- Monitor prices or time displays in stores for compliance or dynamic pricing alerts
- Count inventory by scanning numbered packaging
All this is achieved with minimal human involvement. While the increase in speed or accuracy may seem insignificant at first, it becomes a game changer when a shoplifter is recognised while still in the showroom—or shortly after they have left. Even more critically, timely alerts can help prevent tragedies such as stampedes or riots.
Anything new?
In the context of security cameras, Gen AI means that the camera is no longer just a passive observer. It becomes an active participant in security, learning from past incidents to predict and prevent future ones. This is particularly useful in surveillance, where predicting suspicious behaviour or identifying hidden threats can be as important as detecting them in real time. AI models, for instance, can be trained to identify unusual movements—such as someone loitering in an area for too long or entering restricted zones. Over time, these systems refine their understanding of behavioural patterns, reducing false alarms and improving accuracy.
The Dahua DeepSense face detection and recognition network cameras integrate cutting-edge AI technology to deliver accurate and real-time face detection, tracking, and recognition. Powered by deep learning algorithms, these cameras can identify multiple faces simultaneously, even in challenging lighting or crowded environments. Key features include real-time alerts, facial attributes analysis (such as age, gender, and emotion), and efficient database management for secure access control. Ideal for smart surveillance and business intelligence, DeepSense cameras enhance security, streamline operations, and support intelligent decision-making.
The Arlo 5S camera offers AI-powered features that strengthen home security through intelligent detection and real-time alerts. It uses smart object recognition to differentiate between people, animals, vehicles, and packages, thereby reducing false alarms. The camera includes activity zone customisation and learns user preferences over time to improve notification accuracy. With AI-driven motion detection and cloud-based learning, the Arlo 5S delivers responsive and reliable home surveillance.
Artificial intelligence is revolutionising security cameras by enabling them to learn from their environment, leading to major advances in surveillance capabilities. These cameras can recognise patterns, detect anomalies, and identify specific objects or individuals—thereby significantly strengthening security systems. The Hanwha Techwin Wisenet P Series offers AI-powered surveillance with deep learning-based analytics. These cameras detect and classify people, vehicles, and faces in real time, while minimising false alarms. With up to 4K resolution, wide dynamic range, and excellent low-light performance, they ensure clarity in all conditions. AI metadata tagging further enhances search efficiency and supports faster, smarter investigations.
One major breakthrough is on-device learning—or edge AI—where cameras process data locally instead of relying on the cloud. This shift reduces latency and enhances data privacy. With edge processors like NVIDIA Jetson or Google Coral, AI-enabled cameras can:
- Adapt to new numbers or formats (like a local license plate change).
- Train on unique environments, understanding what’s ‘normal’ and flagging anomalies.
- Refine accuracy over time, reducing false alarms and missed detections.
AI cameras are fast evolving and offer features far beyond traditional surveillance. Highlights include:
Smart analytics
AI cameras analyse video feeds in real time to assess crowd density, detect abnormal behaviour, or even conduct sentiment analysis. Businesses gain actionable insights—for example, retailers can optimise layouts based on customer movement patterns.
Smarter focus
These cameras learn the layout of a space, adjusting their field of view automatically to improve coverage in dynamic environments—whether in a city street or a remote location.
Deep-sea effect
Some models maintain high-quality image capture in extreme lighting, including underwater and low-light settings—a feature referred to as the ‘deep sea effect.’ Enhanced processing algorithms enable applications in marine settings and low-visibility surveillance.
Facial recognition
Now widely adopted, facial recognition technology allows AI-powered cameras to identify individuals in seconds, even in crowds. Use cases range from secure building access to criminal investigations.
Predictive analytics
By analysing historical data, AI systems can forecast potential incidents. Unusual traffic patterns, suspicious movement, or crowd build-up can trigger proactive alerts—enabling early intervention.
Several well-known companies are at the forefront of integrating AI into their security solutions. Panasonic’s AI cameras claim to detect abnormal human behaviour, enabling real-time alerts and more effective crime prevention. Hikvision claims that their AI cameras come with features like automatic number plate recognition (ANPR), people counting, and heat maps, making them highly effective for retail, traffic monitoring, and smart city applications. Their approach includes both edge and cloud AI solutions, enabling flexibility depending on the user’s needs. CP Plus is also claiming similar features for their AI cameras.
| How server-on-Edge AI works? |
| The server-on-edge AI architecture operates through a series of interconnected processes. It begins with video stream input, where security cameras capture real-time video feeds and transmit them to a central server for processing. Once the footage reaches the server, it undergoes AI processing, which involves three key stages: preprocessing (including filtering, noise reduction, and frame extraction), inference (where AI models analyse the video to detect potential threats or anomalies), and postprocessing (which generates actionable insights such as alerts or detailed reports). Following analysis, the storage and retrieval phase ensures that processed data is securely stored in structured formats like SQL databases or object storage systems, facilitating easy access for historical analysis. Finally, in the event that a threat is identified, the system initiates the alerting and response mechanism, automatically notifying security personnel through applications, emails, or alarm systems to ensure swift action. |
The future
Combining edge AI for real-time detection with server-based deep learning will further enhance surveillance. Cloud AI platforms like AWS Rekognition and Google Vision AI are enabling scalable, adaptable security solutions. Cameras are continuously improving as they learn from past incidents, boosting both accuracy and responsiveness.
Autonomous drones and robots with AI-enhanced vision will assist in patrolling and handling security threats. Simultaneously, compact edge AI cameras are making high-performance surveillance more accessible for small businesses and homes.
An exponential rise in the adoption of AI-powered cameras is anticipated. The potential of these technologies is only beginning to unfold.
Imagine retail signage adjusting in real time based on crowd mood, or smart city cameras detecting not just traffic offences but burnt-out lights or damaged road signs by reading and interpreting labels.
In short, the camera of tomorrow will not just see—it will understand.
Akanksha Sondhi Gaur is Senior Technology Journalist at EFY with a German patent to her credit. She has seven years of industrial and academic experience, and has penned several research papers.
