How Does Intelligent Video Analytics Technology Work?
Learn about the end to end process of intelligent video analytics, what it is and how it works. Learn about the technology behind IVA.
What is Intelligent Video Analytics Technology?
Intelligent video analytics technology is an end to end pipeline. It starts with photons hitting an image sensor and ends with a structured event delivered to a VMS, an alarm receiving centre, or a monitoring dashboard. In between, the system runs a sequence of algorithms: image signal processing, compression, inference, tracking, behaviour logic, and event routing.
The term “AI” usually refers to the inference stage, where a deep learning model processes frames and outputs detections. But overall system performance depends just as much on camera optics, encoder settings, and rule tuning as it does on the neural network.
TLDR:
Intelligent video analytics technology works by processing video frames, running deep learning inference to detect and classify objects, tracking those objects over time, applying rules and behaviour models to determine whether an event is meaningful, and then generating alerts with evidence for verification. The same pipeline can run on camera hardware, on servers, or in the cloud, with tradeoffs in latency, bandwidth, and compute cost.
What You Will Learn
You will learn the full pipeline from camera to alert, the difference between classic computer vision and modern deep learning, what tracking and re identification do, how false alarm reduction is engineered, and how edge analytics differs from server and cloud.
How Does Intelligent Video Analytics Technology Work?
In the following sections of this guide to how CCTV video content analysis technology works, we cover the key features and components of this Ai driven software.
If you’re interested in learning the technical details about this threat detection technology, scroll through the rest of this short article to learn more.
For less technically minded readers, our guide to how CCTV analytics works and the benefits it offers, is an easier read.
From Sensor To Frames
Before AI sees anything, the camera converts light into a digital stream.
Image signal processing and optics
The image sensor output is shaped by exposure, gain, white balance, noise reduction, HDR or WDR, and sharpening. Poor tuning here makes inference worse because the model sees blur, smear, or compression artefacts rather than clean features. Lens selection and field of view affect pixel density on target, which determines whether a person at the fence line is represented by enough pixels to classify reliably.
Compression and transport
Video is typically encoded using H.264 or H.265 and transported over RTSP or vendor specific protocols into a VMS or analytics engine. Compression settings such as GOP length and bitrate influence the quality of frames presented to inference. Excess compression can cause blockiness that reduces detection reliability, especially for small targets.
Deep Learning Inference
Inference is where the model produces detections.
Model families used in security analytics
Most modern detectors are convolutional neural network based or transformer based architectures trained to output bounding boxes and class labels. Training requires labelled datasets that represent the target environment: outdoor fence lines, low light conditions, glare, rain, and thermal imagery when used.
Hanwha’s edge AI white paper describes deep learning based architectures that detect predefined objects such as people and vehicles and mark them with bounding boxes.
Confidence scores and thresholds
Detectors output confidence scores. Thresholds determine sensitivity.
Lower thresholds detect more but increase false positives.
Higher thresholds reduce noise but risk missing targets.
Production deployments often use different thresholds for day versus night.
Learn More: What is Latency, Accuracy & Confidence Scoring
Tracking, Association, And Behaviour Logic
Detection is not enough. Tracking builds continuity.
Multi object tracking
Tracking associates detections across frames using motion models and appearance features. It produces tracks with direction, speed, and dwell time. This enables line crossing detection, loitering detection, and “approach” alerts.
Behaviour analysis and rule engines
Rules convert tracks into events. Examples include direction constrained crossing, zone entry with minimum dwell time, or restricted area presence outside operating hours. Some systems also use anomaly detection, comparing current activity to baseline patterns.
Evidence, Verification, And Event Routing
The final stage is delivering an event in a form that supports fast decisions.
Alert packaging
Alerts usually include a snapshot, a short clip, a timestamp, camera ID, zone, object type, and confidence. For remote monitoring, this is what enables rapid verification and escalation.
Integration points
Events are published into VMS, PSIM, alarm platforms, or monitoring centre tooling via SDKs, ONVIF events, REST APIs, or vendor plugins. Milestone demonstrates integration of BriefCam analytics into XProtect, highlighting real time alerts and forensic search workflows.
Summary: How Does Intelligent Video Analytics Technology Work?
Intelligent video analytics is a pipeline: optics and ISP, compression, deep learning inference, tracking, rules, and event distribution. To build systems that perform in real environments, you tune the entire chain, not just the AI model.
