What Are The Different Types of Ai Surveillance Cameras?

A Short Guide to Ai Surveillance Cameras

Ai surveillance cameras are not a one size fits all device. The label ‘Ai’ usually means the camera includes onboard compute capable of running inference, often on a dedicated NPU or GPU within the camera system on chip. The practical difference is where intelligence lives: accurate threat detection and verification.

Traditional CCTV surveillance cameras deliver video to a server where analytics runs. On the other hand, Ai cameras perform detection at the edge and transmit events, metadata, and only the video needed for evidence. This matters for bandwidth, latency, and resilience.

To evaluate Ai security cameras properly, you need to understand the hardware architecture, the analytics functions provided, and the integration model with your VMS and monitoring centre.

What You Will Learn

• the main types of Ai cameras

• complex compute architecture that enables them

• how edge analytics changes system design

• what to prioritise for perimeter detection and monitoring

What is an Ai Surveillance Camera?

Ai surveillance cameras use artificial intelligence and machine learning algorithms to automatically detect and verify security threats. There are a number of cameras and software tools that form a well designed surveillance analytics system.

Advanced detection solutions

In summary, these include edge analytics for object detection, line crossing and intrusion zones, Ai PTZ cameras for auto tracking, thermal Ai cameras for low light detection, and specialised cameras for ANPR or facial recognition.

In terms of the devices, the differences are driven by onboard compute, sensor type, lensing, analytics support, and integration into VMS and verification alarm workflows.

What Are the Types of Ai CCTV Cameras?

In the following sections of this guide, you’ll learn about:

• Edge Ai cameras

• Perimeter surveillance

• Thermal CCTV analytics

• Object detection and tracking

• ANPR and licence plate recognition

We’ll work through the key features, applications and other important information about each type of Ai powered surveillance device.

Edge Ai Cameras for Object Detection

Onboard deep learning and NPUs.

Modern Ai cameras run deep learning models on device. Hanwha describes deep learning based architectures that detect objects like people and vehicles in real time and mark them with bounding boxes.

This is typically enabled by an NPU integrated into the camera SoC, allowing inference without streaming full resolution video to a server.

Object analytics feature sets.

Object analytics typically includes person and vehicle detection, classification, and tracking. Axis describes its object analytics as detecting, classifying, tracking, and counting objects.

Perimeter Focused Ai Surveillance Cameras

Intrusion zones, line crossing, and loitering.

Perimeter Ai cameras provide virtual line crossing detection plus behaviour analytics such as dwell time and direction of travel. These are tuned for fence lines, approach routes, and restricted areas where false alarms are common.

Low light and WDR design choices

Perimeter cameras typically prioritise strong low light performance, WDR, and stable exposure behaviour to maintain inference quality at night. Sensor size, pixel pitch, and IR design can matter more than headline megapixels.

Thermal CCTV Cameras

Heat based detection.

Thermal CCTV surveillance cameras detect heat signatures and run analytics to classify intrusions. FLIR positions thermal security cameras with onboard analytics and PTZ tracking for perimeter intrusion detection.

Best use case.

Thermal CCTV analytics is strongest for early detection, especially in darkness, fog, and poor weather. Pair with visible cameras for identification.

PTZ Cameras & Auto Tracking Systems

Tracking logic and handoff.

Ai PTZ cameras either run analytics on the device itself or via integrated software. The goal is to maintain visual lock for verification and evidence.

Operational value.

Auto tracking reduces operator workload and improves evidential capture, particularly in large open yards.

Alternative Options

ANPR and vehicle intelligence.

ANPR cameras include IR illumination tuned for plates, high shutter speed control, and recognition algorithms. They provide audit trails and access control support, and can be combined with perimeter detection to flag suspicious vehicle approaches.

Facial recognition and access workflows

Facial recognition cameras are typically deployed where consent, policy, and governance are clear. In security architectures, they are often used more in access control and identity verification contexts than as general perimeter detectors.

Summary

Ai surveillance cameras fall into distinct types: edge detection cameras, perimeter analytics cameras, thermal Ai cameras, Ai PTZ tracking systems, and specialised ANPR or facial recognition cameras. Selecting the right type of CCTV analytics solution requires matching hardware capability, sensor and lens design, analytics functions, and integration workflow to the environment you are protecting.